Gunawan dan I Ketut Eddy Purnama ICGC RCICT 2010
Proceedings of The First International Conference on Green Computing
and The Second AUN/SEED-Net Regional Conference on ICT
2010
Department of Electrical Engineering
and Information Technology
Faculty of Engineering
Gadjah Mada University
Jalan Grafika no 2 Kampus UGM
Yogyakarta, 55281, Indonesia
ISSN: 2086-4868
2-3 March 2010
ICGC-RCICT
2010
PROCEEDINGS OF
THE FIRST INTERNATIONAL CONFERENCE
ON GREEN COMPUTING
AND
THE SECOND AUN/SEED-NET
REGIONAL CONFERENCE ON ICT
DEPARTMENT OF ELECTRICAL ENGINEERING
AND INFORMATION TECHNOLOGY
FACULTY OF ENGINEERING
GADJAH MADA UNIVERSITY
ICGC-RCICT 2010
ISSN: 2086-4868
Table of Contents
Inner Cover
Organizing Committee
Foreword
Schedule
Table of Contents
i
ii
iii
v
ix
Part 1: SPECIAL LECTURE
Lec
#1
Energy Efficiency: A Mandatory Driver for a Changing World
Aris Dinamika
1
Lec
#2
Experimental Platform in 5-GHz Band Wireless Communication Using an 80-MHz Bandwidth
MIMO-OFDM System
Singo Yoshizawa, Shinya Odagiri, Yasuhiro Asai, Takashi Gunji, Takashi Saito,
Yoshikazu Miyanaga
13
Lec
#3
Virtual Reality and Augmented Reality and their Applications to Medical Diagnosis and Green
Computing
Kazuhiko HAMAMOTO
17
Lec
#4
Ubiquitous Sensor Networks for Monitoring the Environment: Adaptive Sensing Based on Profiles
Yoshiteru Ishida
23
Lec
#5
Hierarchical Consensus for Large Scale Networked Dynamical Systems
Shinji Hara
28
Lec
#6
Filter Multicast: A Communication Support for Dynamic Vehicle Platoon Management
Hiroshi Shigeno and Ami Uchikawa
34
Part 2: TECHNICAL PAPERS
CAM
#1
Analysis of Single-Phase Passive and Active EMI Filter Performance for Induction Motor Drive
Chanthea Khun, Werachet Khan-gnern, Masaaki Kando
41
CAM
#2
Dynamic Modeling and Control of a SEPIC Converter in Discontinuous Conduction Model
Vuthchhay Eng and Chanin Bunlaksananusorn
45
CAM
#3
Prototype of Sound-based News on Demand Application in Mutli-languages for partial/fully illiterates
Annanda Thavymony RATH
52
INA
#1
Experiences in Implementing General-Purpose Applications on CUDA
Achmad Imam Kistijantoro
56
IND
#1
Epitomizing Green Computing
P.U. Bhalchandra , Dr.S.D.Khamitkar , N.K.Deshmukh , S.N.Lokhande
60
JPN
#1
Range-Free Localization Algorithm Using Distance Deviation Weighted Factor in Wireless Sensor
Network
Thida Zin Myint, Tomoaki Ohtsuki
68
JPN
#2
Stereo-Based Ground Plane Estimation: A Reliable Approach for Low Texure Stereo Images
Sach Thanh LE, Kazuhiko HAMAMOTO, Kiyoaki ATSUTA, Shozo KONDO
74
LAO
#1
3D Reconstruction from X-ray Fluoroscopy using Exponential Opacity Setting for Differential Volume
Rendering
Khamphong Khongsomboon, Phoumy Indarak, Saykhong Saynasine, Kazuhiko HAMAMOTO,
Shozo KONDO
82
Yogyakarta, 2 – 3 March 2010
ix
ISSN: 2086-4868
ICGC-RCICT 2010
MAS
#1
Flat Fibre Technology: Towards a Truly Flexible, Distributed Optical Sensor for Environmental
Protection and Preservation
F.R. Mahamd Adikan, S.R. Sandoghchi
88
MAS
#2
Compact Bismuth-based Erbium-doped Fiber Amplifier With Wide-band Operation
S. W. Harun, X. S. Cheng, H. Ahmad
92
MAS
#3
Adopting Variable Strength Interaction Testing: Some Practical Issues
Kamal Z. Zamli, Mohammed I. Younis
96
MAS
#4
Optimum Operation of Pump at Water Treatment Plant for Achieving Energy Saving
Mohamad Aris Ramlan, Amin Parvizi, Mohamad Rom Tamjis
102
MYA
#1
Sustainable Development of ICT for Rural Areas in Myanmar
Khin Mar Soe
107
MYA
#2
Green in ICT Utilization for Sustainable Development of Myanmar
Soe Soe Khaing
112
PHI
#1
KineSpell2 A Full VAK Approach in Learning Spelling
Rose Ann Sale, Manica Dimaiwat, Ma. Rowena Solamo, Rommel Feria
117
PHI
#2
Event Driven Reconfigurable Architecture for Real-time Multiple Human Motion Tracking and
Profiling
Cesar A. Llorente
121
SIN
#1
Fair End-to-end Bandwidth Allocation (FEBA) Algorithms Observation and Improvement
Nhan H. Truong, Trang T.T. Do, Duong Tran
126
SIN
#2
Cross Directional Rectangle Search for Fast Block-Matching Motion Estimation
Binh P. Nguyen, Trang T. T. Do
132
SIN
#3
A High-Accuracy and High-Speed 2-D 8x8 Discrete Cosine Transform Design
Trang T.T. Do, Binh P. Nguyen
135
THA
#1
Error Concealment in H.264 Spatial Scalable Video using Improved Motion Estimation
Simon Jude Q. Lam, Supavadee Aramvith
140
THA
#2
Evaluation of Blind Modulation Detection in Adaptive OFDM Systems
Donekeo LAKANCHANH, Shingo YOSHIZAWA, Suthichai NOPPANAKEEPONG, Yoshikazu
MIYANAGA
142
THA
#3
An Algorithm for Q-Factor Evaluation A Case Study on 40 Gbps Hybrid Amplified Optical
Transmission System
Nayot Kurukitkoson
147
THA
#4
On-line Verification Algorithm for Flexible Interval Representation System
Napat Rujeerapaiboon, Athasit Surarerks
152
THA
#5
Active Contour Using Local Region Based Force with Adaptive Length Search Line
Sopon Pumeechanya, Charnchai Pluempitiriyawej, Saowapak Thongvigitmanee
157
THA
#6
Running Network Intrusion Detection System on a Recycled Personal Computer
Pornchai Korpraserttaworn and Surin Kittitornkun
163
THA
#7
Directional Filtered Fingerprint Images: An Investigation and Evaluation of Minutiae Consistency
Keokanlaya Sihalath, Somsat Choomchuay, Kazuhiko Hamamoto
167
VIE
#1
A Proposal of Internet-based Monitoring and Control Systems Using OPC UA
Nguyen Thi Thanh Tu, Bui Quoc Khanh, Huynh Quyet Thang
172
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Yogyakarta, 2 – 3 March 2010
ICGC-RCICT 2010
ISSN: 2086-4868
VIE
#2
Providing Qualified Intensional Answers Using Fuzzy Concept Hierarchies
Phong Tuan Ngo, Phuong Hong Nguyen, Anh Kim Nguyen
177
VIE
#3
Credibility Checking-Based Scheduling Schemes for Desktop Grid Computing
Son Hong Ngo, Masaru Fukushi, Xiaohong Jiang
186
VIE
#4
New Method to Implement Intelligent Street Lighting System in Vietnam
Khoi Phan-Dinh
190
VIE
#5
High-throughput Pattern Matching Engine for Network Intrusion Detection System
Tran Ngoc Thinh, Surin Kittitornkun
194
Bali
#1
SMS-Based Technology for Data School Collecting in Bali
I Ketut Gede Darma Putra, I Putu Agung Bayupati, AA. Kompiang Oka Sudana
199
Jaka
#1
Design and Implementation of Solar Tracker in Solar Energy Conversion Systems
Hadian Satria Utama, Endah Setyaningsih, John Son
205
Jaka
#2
The Assesment of Quality of Service (QoS) in IP/MPLS Network
Yulianus, Riri Fitri Sari
209
Madu
#1
Smile Stages Recognition in Orthodontic Rehabilitation Using 2D-PCA Feature Extraction
Rima Tri Wahyuningrum, Mauridhi Hery Purnomo, I Ketut Eddy Purnama
214
Maka
#1
The Internet Protocol Design Framework to Real Time Communication Application Development
Mingsep Sampebua, Lukito Edi Nugroho, Jazi Eko Istiyanto
217
Pale
#1
Reliability Measurement of Internet Services
Deris Stiawan, Abdul Hanan, Mohd. Yazid Idrus
225
Pale
#2
An Integrated Model of Collaborative Learning in Higher Education
Rendra Gustriansyah
230
Papu
#1
The Evaluation of Non-fundamental Frequency Apparent Power and Fundamental Unbalanced Load
Apparent Power Measurements According to IEEE Standard 1459-2000 in the Big Consumers
Maurits A. Paath, Suharyanto, Tumiran, Avrin Nur Widyastuti
237
Sala
#1
Data Authentication in Network Forensic Using MD5 and CRC32 Method
Irwan Sembiring, Jazi Eko Istiyanto
245
Sema
#1
Application of Decision Support System to Determine the Level of Drug-Resistance Tuberculosis
Sari Wijayanti, Adi Setiya Dwi Grahito, Kuwat Triyana
253
Sema
#2
Image Processing Intelligent System Iris Eye Real-Time Compound Distribution Planning At Eye
Center Sultan Agung Hospital
Ida Widihastuti, Sari Ayu Wulandari
257
Sura
#1
Energy-Efficient Protocol of Wireless Sensor Network using Carrier Signaling
A. Sjamsjiar Rachman, Wirawan, Gamantyo Hendrantoro
264
Sura
#2
Performance Evaluation of Adaptive Coded Modulation and Maximal Ratio Combining in MillimeterWave Fixed Cellular System Under the Impact of Rain Attenuation and Interference in Tropical
Region
S. Tahcfulloh, Suwadi, G. Hendrantoro
272
Sura
#3
Efficiency Comparison on Eclat, FP-Tree, and Top-Down Algorithms in Search L-Itemsets
Gunawan, I.K.E Purnama
276
Sura
#4
Collocation Detection for Indonesian
Gunawan, Andy Raharja Tanaya
281
Yogyakarta, 2 – 3 March 2010
xi
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ICGC-RCICT 2010
Sura
#5
Optimization of Size and Location of Capacitor Banks on Distribution Systems Using an Improved
Adaptive Genetic Algorithm
Patria Julianto, Imam Robandi
285
Sura
#6
Position Control of Manipulator’s Links Using Artificial Neural Network with Backpropagation
Training Algorithm
Thiang, Handry Khoswanto, Tan Hendra Sutanto
290
Sura
#7
Maximum Power Point Tracker Using Buck-Boost Converter as Variable Resistance
Vita Lystianingrum, Hendra Hardiansyah, Mochamad Ashari
295
Sura
#8
Filtering of normal, and laryngectomiced patient voices using ANFIS: A Preliminary evaluation of
ANFIS algorithm compared to LPF, median, and average Filter
Andy Noortjahja, Tri Arief Sardjono, MH Purnomo
299
Sura
#9
Compression and Reconstruction of Digital Dental Image on Fuzzy Transform
Ingrid Nurtanio, I Ketut Eddy Purnama, Mauridhi Hery Purnomo
304
Sura
#10
Simulation of Desicion Support System for Pricing Grid Enterprise in Renderring Farm Using Neural
Network
Lilik Anifah, Tommy Alma marif, Haryanto, Buddy Setiawan, Darda Insan Alam, I Ketut Edi
Purnama, Mochammad Hariadi, Mauridhi Hery Purnomo
308
Sura
#11
Backpropagation Neural Network based Reference Control Modeling for Mobile Solar Tracker on a
Large Ship
Budhy Setiawan, Mochamad Ashari, Mauridhi Hery Purnomo
314
Sura
#12
Design and Performance Analysis of Speed Controller in Induction Motor with Sliding Mode Control
Mardlijah, Lusiana Prastiwi, M Hery Purnomo
320
Sura
#13
High Performance of Nonlinear DC Motor Speed Control using Backpropagation Neural Network
Saidah, Mohamad Ashari, Mauridhi Heri Purnomo
325
AAU
#1
Knowledge Sharing in Knowledge-Growing-based Systems
Arwin Datumaya Wahyudi Sumari, Adang Suwandi Ahmad, Aciek Ida Wuryandari, Jaka Sembiring
329
AAU
#2
Savings Time Execution Prima Numbers Generator Using Bit-Array Structure
Rakhmat Hidayat, A. Rida Ismu Windyarto, Herdjunanto, Samiadji, Himawan, H
334
ISTA
Design and Implementation of M-Learning for Increasing Flexibility of Learning System
Gatot Santoso, Edy Sutanta, Rifianto Suryawan
340
UNY
#1
Providing User Quality of Service Specification for Communities with Low Connectivity
Ratna Wardani, F. Soesianto, Lukito Edi Nugroho, Ahmad Ashari
345
UNY
#2
ElectroLarynx, Esopahgus, and Normal Speech Classification using Gradient Discent, Gradient discent
with momentum and learning rate, and Levenberg-Marquardt Algorithm
Fatchul Arifin, Tri Arief Sardjono, Mauridhi Hery
351
UNY
#3
Analysis of Green Computing Strategy in University: Analytic Network Process (ANP) Approach
Handaru Jati
358
UNY
#4
Website Quality Evaluation Comparison: An Empirical Study in Asia
Handaru Jati
365
TETI
#11
Adaptive LMS Noise Cancellation of Wideband Vehicle’s Noise Signals
Sri Arttini Dwi Prasetyowati, Adhi Susanto, Thomas Sriwidodo, Jazi Eko Istiyanto
372
TETI
#12
Design of Power Plant Boiler Temperature Monitoring Application using Sound Card as ADC and
Data Acquisition Device
Udayanto Dwi Atmojo, Gotama Edo Priambodo, Umar Sidiq An Naas, Suharyanto,
Avrin Nur Widyastuti
379
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ICGC-RCICT 2010
ISSN: 2086-4868
TETI
#21
Green Analysis : a System Approach A Case Study: Sensor Fault Detection and Isolation of a web
winding system
Herdjunanto Samiadji, Soesianto, Susanto Adhi,Widodo Sri T
384
TETI
#22
Computation Time Reduction as a Strategy to Reduce Energy in Green Design A Case Study: Model
Parameter Estimation on Unwinding Part of a Web Winding System
Herdjunanto Samiadji
380
TETI
#23
Embedded Webserver Applications for Industrial Process Monitoring
Risanuri Hidayat, Bambang Sutopo, Astria Nur Irfansyah, Ery Dwi Kurniawan
396
TETI
#24
PLC-based Power Factor Regulator
Muh. Isnaeni B. S., Aprilyan Wahyu N., Astria Nur Irfansyah
401
TETI
#25
Implementation of Low Cost Modbus Protocol-Enabled Embedded Interfaces for Industrial Data
Communication
Astria Nur Irfansyah, Eny Sukani Rahayu
405
TETI
#26
Gesture-based Mouse Cursor Movement Using Template-Matching: An Early Study
Paulus Insap Santosa
409
TETI
#27
Low-Powered Wireless Sensor Network for Indoor Localization
Widyawan
412
TETI
#28
Developing Multi-applicationed smart card system with ITSO standard
Enas Dhuhri Kusuma, Litasari
418
TETI
#29
Image Processing Using ARM9-based Embedded System Case Study : Color based tracking using
ARM9 processor and camera module
Enas Dhuhri Kusuma, Addin Suwastono, Wahyu Dewanto
421
TETI
#30
QAM Mapper-Demapper for an Adaptive Modulation OFDM: from Learning Model to FPGA-Based
Implementation
Budi Setiyanto, ‘Imaduddin, Iqbal Prayuda Aditya, Mulyana, Astria Nur Irfansyah, Risanuri
Hidayat, Litasari
426
TETI
#31
Information Technology Infrastructure Flexibility and The Enhancement of the Business/IT Strategy
Alignment
Wahyuni
431
TETI
#32
Analysis of Solar Cell Traffic Light System in Kota Yogyakarta
Avrin Nur Widiastuti, T. Haryono, Lesnanto Multa Putranto, Gusti Gandhi
435
Yogyakarta, 2 – 3 March 2010
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ISSN: 2086-4868
Technical Paper
Proceedings of ICGC-RCICT 2010
Efficiency Comparison on Eclat, FP-Tree, and
Top-Down Algorithms in Search L-Itemsets
Gunawan *,**), I.K.E Purnama *)
*)
Email: [email protected] and [email protected]
Department of Electrical Engineering, Faculty of Industrial Technology, Institut Teknologi Sepuluh Nopember
Kampus ITS Sukolilo, Surabaya 60111, East Java, Indonesia.
**)
Department of Computer Science, Sekolah Tinggi Teknik Surabaya
Ngagel Jaya Tengah 73-77, Surabaya 60284, East Java, Indonesia.
Abstract— We present the comparison in efficiency of the
three famous algorithms, Eclat, FP-Tree, and Top-Down, to
search large itemset in huge database. Eclat can reduce the
amount of database scanning. This algorithm is started
from minimal itemset and continued by intersecting among
items. This process is redone until infrequent node is found
(the support is less than minimum support). Top-Down
algorithm is almost the same as Eclat. Items in the database
are joined to be one itemset and they will be combined by
deleting one item in the itemset in turns. The process is done
recursively until it finds a frequent node. FP-Tree algorithm
does the mining process without generating candidate
itemsets. Original database is stored in a tree form, and then
the mining process is done one by one for all frequent items
in database. Those three algorithms have their own benefits
and weaknesses. Eclat and Top-Down algorithm have their
advantages in making branch of the tree. Eclat makes its
branch by checking whether that combination is already in
database or not. On other hand, Top-Down makes its
branch by looking whether the branch is already made by
another node or not. FP-Tree algorithm’s advantage is it
can generate a little bit candidate itemsets unlike apriori.
The weakness of Eclat and Top-Down is in the
characteristic of the database. If the database has a lot of
frequent items and the minimum support value is small, the
combination process will waste the time. The weakness of
FP-Tree is it saves the original database in a tree form, so it
spends a lot of memory.
Keywords-data mining; association rule mining; Eclat;
FP-Tree; Top-Down;
I.
INTRODUCTION
Data mining has provided a huge contribution in the
development of various disciplines. Data mining aims to
find a pattern of a lot of data collection, and has an
important role in corporate decision-making that has a
large-scale work. Corporate transaction data is processed
to discover patterns from these data. Before the pattern is
found, the data must take several steps, such as data
selection, data cleaning and preprocessing, data
transformation, data reduction (elimination of unnecessary
data), data mining task and algorithm selection , postprocessing, and the last is to apply the techniques in data
mining.
One of the tasks of data mining is to discover frequent
item sets. Frequent item sets is pairs of data that often
arise. Frequent itemset has important role in data mining
that in the application to find specific patterns in the
database, such as association rule, correlation, sequence,
276
episode, classifier, and cluster. Of all these patterns, one
of the highlights is the association rule. Association rule
based on searching of motivation to do an analysis of
transactions in supermarkets. From the analysis of these
transactions can studied about customer behavior in
shopping. Association rule produces conclusions about
how often a product is purchased in conjunction with
other products.
Data set
L-Itemsets Search
Algorithms
Large
Itemsets
Generate
Association Rule
Association
Rule
Figure 1. Association Rule Mining Process
Figure 1 shows that the data mining process starts
from the preparation of data sets. Then the filtered data is
brought to the next process that is selection of appropriate
algorithms to search Litemset (Large Itemset).
There are various algorithms that can be used to
discovery of large litemset. But in this study only Eclat
algorithm, FP-Tree, and Top-Down will be discussed.
II.
RELATED THEORIES
A. FP-Tree Algorithm
In this chapter will discuss about searching frequent
pattern using the Frequent Pattern Tree (FP-Tree)
algorithm. Input from the FP-Tree algorithm consists of
two fields, which is the index field and target fields.
In this case, field index only use to sort the data. In the
FP-Tree algorithm, there is no candidate generation
process. This is caused by the formation of candidate
generation is a factor that causes delays. Large Itemset
Dept. of EE and IT, GMU
Yogyakarta, 2 - 3 March 2010
Proceedings of ICGC-RCICT 2010
Technical Paper
search process by using the FP-Tree algorithm can be seen
in figure 2.
Data set
number of frequencies. If the frequent items are
sorted by frequency of occurrence desendingly the
possibility of the emergence of the same prefix
string will be greater.
2) Example of Tree Formation
The following will be given a transaction database,
database is to clarify the construction of the FP-Tree. The
database contains information of transaction id and item
purchased as shown by the first two columns of table 1
and a minimum support İ .
Tree Construction
3:1
FP-Tree
The first step performed in FP-Tree algorithm, which
is scanning the database to get the whole first frequent
itemset and record the frequency of each item and it
compared with the minimum support. If the frequency of
itemset is below the minimum support then the itemset
will not be used in next process.
Conditional Pattern
Search
3:3
Conditional
Pattern
Frequent itemset that has been obtained then sorted
descendingly based on their frequency. The next step is to
form the root of the tree is initialized with NIL. Database
then scanned for the second time. Sequencing performed
on the database on each transaction. The items on each
transaction are sorted according to items name that are
ordered based on the results of the previous process.
Sequencing results in a database transaction can be seen in
Table 1.
FP-Growth
3:4
Large
Itemsets
Figure 2. Large Itemset Search Process with FP-Tree
FP-Tree algorithm is an algorithm for mining the
database which this algorithm minimizes the candidate
search process. Candidate is search in the FP-Tree which
is only often appears. Otherwise, FP-Tree use tree
structure so it can avoid reading the database repeatedly.
This is certainly different from Apriori algorithm that
doing readings database continuously. Apriori algorithm
is waste time.
1) Tree Construction
Data structure on FP-Tree must be right so that it can
accommodate all the information required in the mining
process accurately. Making data structures in the FP-Tree
should be drawn up based on some facts that made
observations as follows:
x
Database search must be done at one time only.
Search this database must be able to classify the
items that included in the frequent itemset. The
frequency of appearance of these items should
also be stored in addition to the name of the item
itself. This is done because the mining process is
only done on frequent item only. Frequent items
will be removed.
x
If the structure of frequent items storage of each
transaction is right then search the database
repeatedly can be avoided.
x
Transactions that have set of the same frequent
item can be merged into one and only need to
increase the frequency. To be able to find two sets
of item are same or not, each transaction must be
ordered either in ascending or descending.
x
In two transactions that have the same common
prefix based on a particular item frequent
sequences, the same parts can be combined into a
single structure with the same prefix include the
Yogyakarta, 2 - 3 March 2010
ISSN: 2086-4868
TABLE I.
TID
EXAMPLE OF TRANSACTION DATABASE
Items
Bought
Frequency (ordered)
100
f; a; c; d; g; i; m; p;
f; c; a; m; p;
200
a; b; c; f; l; m; o;
f; c; a; b; m;
300
b; f; h; j; o;
f; b;
400
b; c; k; s; p;
c; b; p;
500
a; f; c; e; l; p; m; n;
f; c; a; m; p;
After reading the database for the second time, then do
the reading of the transaction. First of all, the first
transactions will be read, so will get the set of frequent
items (f, c, a, m, p). In the first transaction, all of the
frequent items always inserted as a new node into the tree
and it will be forming the first path tree. For the first time
link for each node is always null. Next, all of frequent
items wil bee inserted in header table. Item name inserted
in the item column, the number of frequent items inserted
in a count column, and the head links column pointing to
each formed node. In the first transaction, the count value
Dept. of EE and IT, GMU
Figure 3. First Step of FP-Tree
277
ISSN: 2086-4868
Technical Paper
of all rows equal to one. Figure of FP-Tree formation of
the first transaction can be seen in figure 3. Process
conducted in the same way until the fifth transaction.
3) Mining Process with FP-Tree
Generally, the process of mining using FP-Tree can be
divided into 4 steps. The steps are intended as follows:
1.
Mining in the FP-Tree starts from an item with
the smallest support in the header table.
2.
Find frequent items that contain items that will be
mining.
3.
Look k- conditional pattern base for k items that
will be mining.
4.
Form of conditional FP-Tree based on the
conditional pattern base that has been formed
previously.
Proceedings of ICGC-RCICT 2010
B. Eclat Algorithm
Eclat algorithm was first introduced by Mohammed
Javeed Zaki in 1997. The emergence of this algorithm
must not be separated from the Apriori. In the Eclat
algorithm does not perform database read repeatedly as in
the Apriori algorithm.
Input for Eclat algorithm consists of two fields, index
field and target fields. Large Itemset search process by
using the Eclat algorithm can be seen in figure 4.
Data set
Search and Sort
Frequent Item
4) Conditional Pattern Base Search
After making the formation of the tree that has been
given before which is based on mining step in the FPTree, the next thing to do is do a search conditional
pattern base of each item that has been listed in the case
example of table 1. The following example is to mining
on the p-conditional pattern base.
The first mining in a given case starting from the node
p. What to do is look at the header table, head-link from
node p starts from where and to where. Based on the
observations obtained frequent patterns containing p,
which is (f: 4, c: 3, a: 3, m: 2, p: 2) and (c: 1, b: 1, p: 1). In
the first path is shown that the path (f, c, a, m, p) appears
twice in the database. Path (f, c, a) appears three times and
the path (f) itself appears four times but only appeared
twice together with the p. Therefore, the calculated path
prefix is (f: 2, c: 2, a: 2, m: 2, p: 2).
While the path (c: 1, b: 1, p: 1), the path prefix used is
(c: 1, b: 1). Of the two prefix paths will be formed p’ sub
pattern base termed p’ conditional pattern base.
Definition of p' conditional pattern base is a sub-pattern
that occurs with the following conditions of p.
From the prefix path obtained for node p, then
performed the counting support of the items contained in
the prefix path. The results support the calculation of each
item can be seen in Table 2.
TABLE II.
P-CONDITIONAL PATTERN BASE
Item
Amount
f
2
c
3
a
2
m
2
b
1
Previously been known that the minimum support
from the example of table 1 is 3. So from Table 2 can be
seen that the eligible item is item c so that item c only the
frequent items. Next is the formation of p conditional FPTree. From table 2 notice that only items c that qualifying
the requirements, then p-conditional FP-Tree consists of
only one branch only.
278
Frequent
Item
Combination Process
for each Frequent
Item
Large
Itemsets
Figure 4. Large Itemset Search Process with Eclat
1) Mining Process with Eclat
Mining process using the Eclat algorithm is basically
done by taking slices among the items that qualifying the
minimum support value. Operation frequent wedge
between these items will certainly accelerate the process
of mining by using the Eclat algorithm. If the frequent
itemset is more longer then the number of slices
operations performed will be less, so join process will be
fewer as well.
Eclat algorithm does not require complex hash-tree
structure. Therefore, the memory used by Eclat algorithm
is not too much. Even Eclat algorithm has the advantage
to allocate memory properly. It is because of the Eclat
algorithm only needs to be done with a linear scan of the
itemset. In the Eclat algorithm, the smaller value of
minimum support will found the faster frequent itemset.
As a result, mining process is also done faster. This will
be very beneficial because it can reduce operational costs.
C. Top-Down Algorithm
In the previous chapter has discussed about the FPTree and Eclat algorithm. In this chapter will discuss the
final algorithm, the Top-Down algorithm. Discussion of
Top-Down algorithm will contain about how to obtain
mining frequent itemset of the database provided. Large
Itemset search process by using Top-Down algorithm can
be seen in figure 5.
In the Top-Down algorithm, database mining starts
from the maximal itemset. Maximal itemset is the itemset
which the itemset is not part of another itemset. First, the
items search in the database that is qualifying the
minimum support. All frequent items produced will be
Dept. of EE and IT, GMU
Yogyakarta, 2 - 3 March 2010
Proceedings of ICGC-RCICT 2010
Technical Paper
merged into one and begin the process of combination of
items one by one.
Data set
Group and Search
Frequent Item
Combination Process
of Maximal Itemsets
Large
Itemsets
Figure 5. Large Itemset Search Process with Top-Down
1) Mining Process with Top-Down
Mining process by using Top-Down algorithm is
basically done by combining of the mining itemset.
Combination process is meant to remove an item in
itemset interchangeably. New itemset results from the
combination will be mining again repeatedly until
discovered frequent itemset or a search had reached the
last level.
The principle to run Top-Down algorithm is mining
process is done recursively until it was found that the
nodes that are traced are frequent. After getting the
frequent items, the next step is to form probability
combination. New nodes are automatically generated as
frequent itemset. The principle of Top-Down algorithm is
very efficient. This is based on the fact that at the time of
the formation of combinations, Top-Down algorithm
always check on the other branches that if the
combination has been previously established or not.
TESTING AND RESULT
In this chapter will be given a comparison of time and
memory used at the time of mining with Eclat, FP-Tree,
and Top-Down algorithms. Mining for testing program
will use several databases with different support value. So
at each mining on a database will be given some support
value by using the three algorithms.
The process of testing conducted in this study using a
computer with Pentium processor 4IV 1.8 GHz and 256
Mb of memory. The operating system used in this testing
process is Microsoft Windows 2000. Database that will be
used for testing is BMS-WebView-1 database which has
total of 22,225 transactions, and using Movie database
that has total of 5888 transactions.
In this chapter will be divided into two sub-chapters
which the first section will explain about the time
comparison and the second section will explain about the
memory comparison that is used by the three algorithms.
Database that used is Microsoft Access database.
Yogyakarta, 2 - 3 March 2010
A. Time Comparison
Time used to perform mining will vary for each
algorithms. In some case of certain databases may Eclat
algorithm has faster mining times than FP-Tree algorithm
or on other database mining process with Top-Down
algorithm faster than another algorithm. Mining time is
also influenced by how much support is used.
TABLE III.
Maximal
Itemsets
III.
ISSN: 2086-4868
USING BMS-WEBVIEW-1 DATABASE (HH:MM:SS)
Algorithms
Min.
Support
Eclat
FP-Tree
Top-Down
0.02
00:00:53
00:01:49
00:02:43
0.03
00:00:33
00:01:40
00:54:39
0.04
00:00:24
00:01:48
00:00:34
0.05
00:00:12
00:01:55
00:00:01
In table 3 it appears that Top-Down algorithm has the
fastest time compared with Eclat and FP-Tree if the
minimum support that used are 0.04 and 0.05. With that
support, Eclat algorithm when compared with the FP-Tree
has better time mining. However, the smaller of the
support given in the BMS-WebView-1 database mining,
the time mining with Top-Down algorithm worse than
other algorithms. FP-Tree algorithm in this case has a
lapse of time that was stable which is compared with other
algorithms.
TABLE IV.
USING MOVIE DATABASE (HH:MM:SS)
Algorithms
Min.
Support
Eclat
FP-Tree
Top-Down
0.03
00:00:30
00:01:24
12:11:05
0.04
00:00:10
00:00:51
02:41:54
0.05
00:00:02
00:00:09
00:00:00
In table 4, the minimum support used in the
experiments using movie database is 0.3, 0.4, and 0.5.
When use support 0.5, Top-Down algorithm has the
fastest time mining compared to other algorithms.
However, when the minimum support is reduced, the
time mining required for mining with Top-Down become
more greater. In the case of movie database, Eclat
algorithm has small time mining and stable when
compared with FP-Tree and Top-Down algorithm.
B. Memory Comparison
Eclat, FP-Tree, and Top-Down algorithm has
similarity in mining process, the algorithms are the same
conduct tree formation which will be used in the database
mining. The formation of the tree will use the available
memory in computers. This section will discuss about the
comparison of memory used in the mining process using
the Eclat, FP-Tree, and Top-Down algorithm.
Table 5 shows that in experiments using FP-Tree
algorithm requires more memory than Eclat and TopDown algorithm. At first, Top-Down algorithm using the
most memory-efficient than other algorithms (support =
0.05 and support = 0.04), but when support is smaller then
the amount of memory required is greater. Eclat algorithm
Dept. of EE and IT, GMU
279
ISSN: 2086-4868
Technical Paper
has a change in the amount of memory was stable
between the three algorithms.
TABLE V.
USING BMS-WEBVIEW-1 DATABASE
[2]
[3]
Algorithms
Min.
Support
Eclat
FP-Tree
Top-Down
0.02
0.2M
7.3M
3.8M
0.03
0.07M
5.5M
2.6M
0.04
0.05M
5.0M
0.04M
0.05
0.02M
4.6M
0.002M
[4]
[5]
[6]
TABLE VI.
[7]
USING MOVIE DATABASE
Algorithms
Min.
Support
Eclat
FP-Tree
Top-Down
0.03
0.02M
10.0M
20.0M
0.04
0.007M
6.3M
4.7M
0.05
0.002M
1.9M
0.0008M
[8]
Proceedings of ICGC-RCICT 2010
R. Agrawal, R. Srikant, “Mining sequential patterns:
Generalization and performance improvements”, In Proceedings
1st International Conference Knowledge Discovery and Data
Mining, 1993.
C. Borgelt, “Efficient Implementation of Apriori and Eclat”,
Unpublised, Magdeburg: Department of Knowledge Processing
and Language Engineering School of Computer Science, Ottovon-Guericke-University of Magdeburg.
B. Goethals, “Memory Issues in Frequent Itemset Mining”,
Unpublised, Helsinki: Department of Computer Science,
University of Helsinki.
J. Han, J. Pei, Y. Yin, “Mining Frequent Pattern Without
Candidate Generation”, Unpublised, School of Computer Science,
Simon Fraser University.
J. Han, M. Kamber, “Data Mining: Concepts and Techniques”,
1999.
S. Parthasarathy, M.J. Zaki, M. Ogihara, W. Li, “New Algorithms
for Fast Discovery of Association Rules”, New York: Computer
Science Department, University of Rochester, July 1997.
M.J. Zaki, “Scalable Data Mining for Rules”, New York:
Department of Computer Science, University of Rochester, 1998.
In table 6 can be seen that, the process of mining by
using the Eclat algorithm was needed less amount of
memory when compared with other algorithms.
Meanwhile, at a minimum the use of support 0.4 and 0.5,
it need larger memory when using the Top-Down
algorithm and so did the difference amount of memory
required when using support 0.3 and 0.4.
IV.
CONCLUSION
In making this study, authors obtain some conclusions
include:
x
Disappearance process of unfrequent items makes
FPTree formation faster. Elimination of items
produce a new table that contains frequent items
that will accelerate the formation of tree. The
formation of tree no longer access the original
database, but only access the new table.
x
FP-Tree has a fast time mining even use larger
size dataset. While lack of FP-Tree is this
algorithm takes a large memory.
x
Top-Down algorithm does not need to do pruning
like FP-Tree. Top-Down weakness is when the
type of items and lots of frequent items and the
minimum of the support will waste time because
Top-Down continue to search until it found
frequent itemset.
x
Branch formation by using the Eclat algorithm
always look in advance whether the combination
is contained in the database or not. This will avoid
the formation of branches that are not needed. The
weakness is the algorithm always doing support
calculations despite the itemset is already frequent
because this algorithm will stop if found
unfrequent itemset.
REFERENCES
[1]
280
R. Agrawal, R. Srikant, “Fast Algorithms for Mining Association
Rules”, San Jose: IBM Almaden Research Centre.
Dept. of EE and IT, GMU
Yogyakarta, 2 - 3 March 2010
and The Second AUN/SEED-Net Regional Conference on ICT
2010
Department of Electrical Engineering
and Information Technology
Faculty of Engineering
Gadjah Mada University
Jalan Grafika no 2 Kampus UGM
Yogyakarta, 55281, Indonesia
ISSN: 2086-4868
2-3 March 2010
ICGC-RCICT
2010
PROCEEDINGS OF
THE FIRST INTERNATIONAL CONFERENCE
ON GREEN COMPUTING
AND
THE SECOND AUN/SEED-NET
REGIONAL CONFERENCE ON ICT
DEPARTMENT OF ELECTRICAL ENGINEERING
AND INFORMATION TECHNOLOGY
FACULTY OF ENGINEERING
GADJAH MADA UNIVERSITY
ICGC-RCICT 2010
ISSN: 2086-4868
Table of Contents
Inner Cover
Organizing Committee
Foreword
Schedule
Table of Contents
i
ii
iii
v
ix
Part 1: SPECIAL LECTURE
Lec
#1
Energy Efficiency: A Mandatory Driver for a Changing World
Aris Dinamika
1
Lec
#2
Experimental Platform in 5-GHz Band Wireless Communication Using an 80-MHz Bandwidth
MIMO-OFDM System
Singo Yoshizawa, Shinya Odagiri, Yasuhiro Asai, Takashi Gunji, Takashi Saito,
Yoshikazu Miyanaga
13
Lec
#3
Virtual Reality and Augmented Reality and their Applications to Medical Diagnosis and Green
Computing
Kazuhiko HAMAMOTO
17
Lec
#4
Ubiquitous Sensor Networks for Monitoring the Environment: Adaptive Sensing Based on Profiles
Yoshiteru Ishida
23
Lec
#5
Hierarchical Consensus for Large Scale Networked Dynamical Systems
Shinji Hara
28
Lec
#6
Filter Multicast: A Communication Support for Dynamic Vehicle Platoon Management
Hiroshi Shigeno and Ami Uchikawa
34
Part 2: TECHNICAL PAPERS
CAM
#1
Analysis of Single-Phase Passive and Active EMI Filter Performance for Induction Motor Drive
Chanthea Khun, Werachet Khan-gnern, Masaaki Kando
41
CAM
#2
Dynamic Modeling and Control of a SEPIC Converter in Discontinuous Conduction Model
Vuthchhay Eng and Chanin Bunlaksananusorn
45
CAM
#3
Prototype of Sound-based News on Demand Application in Mutli-languages for partial/fully illiterates
Annanda Thavymony RATH
52
INA
#1
Experiences in Implementing General-Purpose Applications on CUDA
Achmad Imam Kistijantoro
56
IND
#1
Epitomizing Green Computing
P.U. Bhalchandra , Dr.S.D.Khamitkar , N.K.Deshmukh , S.N.Lokhande
60
JPN
#1
Range-Free Localization Algorithm Using Distance Deviation Weighted Factor in Wireless Sensor
Network
Thida Zin Myint, Tomoaki Ohtsuki
68
JPN
#2
Stereo-Based Ground Plane Estimation: A Reliable Approach for Low Texure Stereo Images
Sach Thanh LE, Kazuhiko HAMAMOTO, Kiyoaki ATSUTA, Shozo KONDO
74
LAO
#1
3D Reconstruction from X-ray Fluoroscopy using Exponential Opacity Setting for Differential Volume
Rendering
Khamphong Khongsomboon, Phoumy Indarak, Saykhong Saynasine, Kazuhiko HAMAMOTO,
Shozo KONDO
82
Yogyakarta, 2 – 3 March 2010
ix
ISSN: 2086-4868
ICGC-RCICT 2010
MAS
#1
Flat Fibre Technology: Towards a Truly Flexible, Distributed Optical Sensor for Environmental
Protection and Preservation
F.R. Mahamd Adikan, S.R. Sandoghchi
88
MAS
#2
Compact Bismuth-based Erbium-doped Fiber Amplifier With Wide-band Operation
S. W. Harun, X. S. Cheng, H. Ahmad
92
MAS
#3
Adopting Variable Strength Interaction Testing: Some Practical Issues
Kamal Z. Zamli, Mohammed I. Younis
96
MAS
#4
Optimum Operation of Pump at Water Treatment Plant for Achieving Energy Saving
Mohamad Aris Ramlan, Amin Parvizi, Mohamad Rom Tamjis
102
MYA
#1
Sustainable Development of ICT for Rural Areas in Myanmar
Khin Mar Soe
107
MYA
#2
Green in ICT Utilization for Sustainable Development of Myanmar
Soe Soe Khaing
112
PHI
#1
KineSpell2 A Full VAK Approach in Learning Spelling
Rose Ann Sale, Manica Dimaiwat, Ma. Rowena Solamo, Rommel Feria
117
PHI
#2
Event Driven Reconfigurable Architecture for Real-time Multiple Human Motion Tracking and
Profiling
Cesar A. Llorente
121
SIN
#1
Fair End-to-end Bandwidth Allocation (FEBA) Algorithms Observation and Improvement
Nhan H. Truong, Trang T.T. Do, Duong Tran
126
SIN
#2
Cross Directional Rectangle Search for Fast Block-Matching Motion Estimation
Binh P. Nguyen, Trang T. T. Do
132
SIN
#3
A High-Accuracy and High-Speed 2-D 8x8 Discrete Cosine Transform Design
Trang T.T. Do, Binh P. Nguyen
135
THA
#1
Error Concealment in H.264 Spatial Scalable Video using Improved Motion Estimation
Simon Jude Q. Lam, Supavadee Aramvith
140
THA
#2
Evaluation of Blind Modulation Detection in Adaptive OFDM Systems
Donekeo LAKANCHANH, Shingo YOSHIZAWA, Suthichai NOPPANAKEEPONG, Yoshikazu
MIYANAGA
142
THA
#3
An Algorithm for Q-Factor Evaluation A Case Study on 40 Gbps Hybrid Amplified Optical
Transmission System
Nayot Kurukitkoson
147
THA
#4
On-line Verification Algorithm for Flexible Interval Representation System
Napat Rujeerapaiboon, Athasit Surarerks
152
THA
#5
Active Contour Using Local Region Based Force with Adaptive Length Search Line
Sopon Pumeechanya, Charnchai Pluempitiriyawej, Saowapak Thongvigitmanee
157
THA
#6
Running Network Intrusion Detection System on a Recycled Personal Computer
Pornchai Korpraserttaworn and Surin Kittitornkun
163
THA
#7
Directional Filtered Fingerprint Images: An Investigation and Evaluation of Minutiae Consistency
Keokanlaya Sihalath, Somsat Choomchuay, Kazuhiko Hamamoto
167
VIE
#1
A Proposal of Internet-based Monitoring and Control Systems Using OPC UA
Nguyen Thi Thanh Tu, Bui Quoc Khanh, Huynh Quyet Thang
172
x
Yogyakarta, 2 – 3 March 2010
ICGC-RCICT 2010
ISSN: 2086-4868
VIE
#2
Providing Qualified Intensional Answers Using Fuzzy Concept Hierarchies
Phong Tuan Ngo, Phuong Hong Nguyen, Anh Kim Nguyen
177
VIE
#3
Credibility Checking-Based Scheduling Schemes for Desktop Grid Computing
Son Hong Ngo, Masaru Fukushi, Xiaohong Jiang
186
VIE
#4
New Method to Implement Intelligent Street Lighting System in Vietnam
Khoi Phan-Dinh
190
VIE
#5
High-throughput Pattern Matching Engine for Network Intrusion Detection System
Tran Ngoc Thinh, Surin Kittitornkun
194
Bali
#1
SMS-Based Technology for Data School Collecting in Bali
I Ketut Gede Darma Putra, I Putu Agung Bayupati, AA. Kompiang Oka Sudana
199
Jaka
#1
Design and Implementation of Solar Tracker in Solar Energy Conversion Systems
Hadian Satria Utama, Endah Setyaningsih, John Son
205
Jaka
#2
The Assesment of Quality of Service (QoS) in IP/MPLS Network
Yulianus, Riri Fitri Sari
209
Madu
#1
Smile Stages Recognition in Orthodontic Rehabilitation Using 2D-PCA Feature Extraction
Rima Tri Wahyuningrum, Mauridhi Hery Purnomo, I Ketut Eddy Purnama
214
Maka
#1
The Internet Protocol Design Framework to Real Time Communication Application Development
Mingsep Sampebua, Lukito Edi Nugroho, Jazi Eko Istiyanto
217
Pale
#1
Reliability Measurement of Internet Services
Deris Stiawan, Abdul Hanan, Mohd. Yazid Idrus
225
Pale
#2
An Integrated Model of Collaborative Learning in Higher Education
Rendra Gustriansyah
230
Papu
#1
The Evaluation of Non-fundamental Frequency Apparent Power and Fundamental Unbalanced Load
Apparent Power Measurements According to IEEE Standard 1459-2000 in the Big Consumers
Maurits A. Paath, Suharyanto, Tumiran, Avrin Nur Widyastuti
237
Sala
#1
Data Authentication in Network Forensic Using MD5 and CRC32 Method
Irwan Sembiring, Jazi Eko Istiyanto
245
Sema
#1
Application of Decision Support System to Determine the Level of Drug-Resistance Tuberculosis
Sari Wijayanti, Adi Setiya Dwi Grahito, Kuwat Triyana
253
Sema
#2
Image Processing Intelligent System Iris Eye Real-Time Compound Distribution Planning At Eye
Center Sultan Agung Hospital
Ida Widihastuti, Sari Ayu Wulandari
257
Sura
#1
Energy-Efficient Protocol of Wireless Sensor Network using Carrier Signaling
A. Sjamsjiar Rachman, Wirawan, Gamantyo Hendrantoro
264
Sura
#2
Performance Evaluation of Adaptive Coded Modulation and Maximal Ratio Combining in MillimeterWave Fixed Cellular System Under the Impact of Rain Attenuation and Interference in Tropical
Region
S. Tahcfulloh, Suwadi, G. Hendrantoro
272
Sura
#3
Efficiency Comparison on Eclat, FP-Tree, and Top-Down Algorithms in Search L-Itemsets
Gunawan, I.K.E Purnama
276
Sura
#4
Collocation Detection for Indonesian
Gunawan, Andy Raharja Tanaya
281
Yogyakarta, 2 – 3 March 2010
xi
ISSN: 2086-4868
ICGC-RCICT 2010
Sura
#5
Optimization of Size and Location of Capacitor Banks on Distribution Systems Using an Improved
Adaptive Genetic Algorithm
Patria Julianto, Imam Robandi
285
Sura
#6
Position Control of Manipulator’s Links Using Artificial Neural Network with Backpropagation
Training Algorithm
Thiang, Handry Khoswanto, Tan Hendra Sutanto
290
Sura
#7
Maximum Power Point Tracker Using Buck-Boost Converter as Variable Resistance
Vita Lystianingrum, Hendra Hardiansyah, Mochamad Ashari
295
Sura
#8
Filtering of normal, and laryngectomiced patient voices using ANFIS: A Preliminary evaluation of
ANFIS algorithm compared to LPF, median, and average Filter
Andy Noortjahja, Tri Arief Sardjono, MH Purnomo
299
Sura
#9
Compression and Reconstruction of Digital Dental Image on Fuzzy Transform
Ingrid Nurtanio, I Ketut Eddy Purnama, Mauridhi Hery Purnomo
304
Sura
#10
Simulation of Desicion Support System for Pricing Grid Enterprise in Renderring Farm Using Neural
Network
Lilik Anifah, Tommy Alma marif, Haryanto, Buddy Setiawan, Darda Insan Alam, I Ketut Edi
Purnama, Mochammad Hariadi, Mauridhi Hery Purnomo
308
Sura
#11
Backpropagation Neural Network based Reference Control Modeling for Mobile Solar Tracker on a
Large Ship
Budhy Setiawan, Mochamad Ashari, Mauridhi Hery Purnomo
314
Sura
#12
Design and Performance Analysis of Speed Controller in Induction Motor with Sliding Mode Control
Mardlijah, Lusiana Prastiwi, M Hery Purnomo
320
Sura
#13
High Performance of Nonlinear DC Motor Speed Control using Backpropagation Neural Network
Saidah, Mohamad Ashari, Mauridhi Heri Purnomo
325
AAU
#1
Knowledge Sharing in Knowledge-Growing-based Systems
Arwin Datumaya Wahyudi Sumari, Adang Suwandi Ahmad, Aciek Ida Wuryandari, Jaka Sembiring
329
AAU
#2
Savings Time Execution Prima Numbers Generator Using Bit-Array Structure
Rakhmat Hidayat, A. Rida Ismu Windyarto, Herdjunanto, Samiadji, Himawan, H
334
ISTA
Design and Implementation of M-Learning for Increasing Flexibility of Learning System
Gatot Santoso, Edy Sutanta, Rifianto Suryawan
340
UNY
#1
Providing User Quality of Service Specification for Communities with Low Connectivity
Ratna Wardani, F. Soesianto, Lukito Edi Nugroho, Ahmad Ashari
345
UNY
#2
ElectroLarynx, Esopahgus, and Normal Speech Classification using Gradient Discent, Gradient discent
with momentum and learning rate, and Levenberg-Marquardt Algorithm
Fatchul Arifin, Tri Arief Sardjono, Mauridhi Hery
351
UNY
#3
Analysis of Green Computing Strategy in University: Analytic Network Process (ANP) Approach
Handaru Jati
358
UNY
#4
Website Quality Evaluation Comparison: An Empirical Study in Asia
Handaru Jati
365
TETI
#11
Adaptive LMS Noise Cancellation of Wideband Vehicle’s Noise Signals
Sri Arttini Dwi Prasetyowati, Adhi Susanto, Thomas Sriwidodo, Jazi Eko Istiyanto
372
TETI
#12
Design of Power Plant Boiler Temperature Monitoring Application using Sound Card as ADC and
Data Acquisition Device
Udayanto Dwi Atmojo, Gotama Edo Priambodo, Umar Sidiq An Naas, Suharyanto,
Avrin Nur Widyastuti
379
xii
Yogyakarta, 2 – 3 March 2010
ICGC-RCICT 2010
ISSN: 2086-4868
TETI
#21
Green Analysis : a System Approach A Case Study: Sensor Fault Detection and Isolation of a web
winding system
Herdjunanto Samiadji, Soesianto, Susanto Adhi,Widodo Sri T
384
TETI
#22
Computation Time Reduction as a Strategy to Reduce Energy in Green Design A Case Study: Model
Parameter Estimation on Unwinding Part of a Web Winding System
Herdjunanto Samiadji
380
TETI
#23
Embedded Webserver Applications for Industrial Process Monitoring
Risanuri Hidayat, Bambang Sutopo, Astria Nur Irfansyah, Ery Dwi Kurniawan
396
TETI
#24
PLC-based Power Factor Regulator
Muh. Isnaeni B. S., Aprilyan Wahyu N., Astria Nur Irfansyah
401
TETI
#25
Implementation of Low Cost Modbus Protocol-Enabled Embedded Interfaces for Industrial Data
Communication
Astria Nur Irfansyah, Eny Sukani Rahayu
405
TETI
#26
Gesture-based Mouse Cursor Movement Using Template-Matching: An Early Study
Paulus Insap Santosa
409
TETI
#27
Low-Powered Wireless Sensor Network for Indoor Localization
Widyawan
412
TETI
#28
Developing Multi-applicationed smart card system with ITSO standard
Enas Dhuhri Kusuma, Litasari
418
TETI
#29
Image Processing Using ARM9-based Embedded System Case Study : Color based tracking using
ARM9 processor and camera module
Enas Dhuhri Kusuma, Addin Suwastono, Wahyu Dewanto
421
TETI
#30
QAM Mapper-Demapper for an Adaptive Modulation OFDM: from Learning Model to FPGA-Based
Implementation
Budi Setiyanto, ‘Imaduddin, Iqbal Prayuda Aditya, Mulyana, Astria Nur Irfansyah, Risanuri
Hidayat, Litasari
426
TETI
#31
Information Technology Infrastructure Flexibility and The Enhancement of the Business/IT Strategy
Alignment
Wahyuni
431
TETI
#32
Analysis of Solar Cell Traffic Light System in Kota Yogyakarta
Avrin Nur Widiastuti, T. Haryono, Lesnanto Multa Putranto, Gusti Gandhi
435
Yogyakarta, 2 – 3 March 2010
xiii
ISSN: 2086-4868
Technical Paper
Proceedings of ICGC-RCICT 2010
Efficiency Comparison on Eclat, FP-Tree, and
Top-Down Algorithms in Search L-Itemsets
Gunawan *,**), I.K.E Purnama *)
*)
Email: [email protected] and [email protected]
Department of Electrical Engineering, Faculty of Industrial Technology, Institut Teknologi Sepuluh Nopember
Kampus ITS Sukolilo, Surabaya 60111, East Java, Indonesia.
**)
Department of Computer Science, Sekolah Tinggi Teknik Surabaya
Ngagel Jaya Tengah 73-77, Surabaya 60284, East Java, Indonesia.
Abstract— We present the comparison in efficiency of the
three famous algorithms, Eclat, FP-Tree, and Top-Down, to
search large itemset in huge database. Eclat can reduce the
amount of database scanning. This algorithm is started
from minimal itemset and continued by intersecting among
items. This process is redone until infrequent node is found
(the support is less than minimum support). Top-Down
algorithm is almost the same as Eclat. Items in the database
are joined to be one itemset and they will be combined by
deleting one item in the itemset in turns. The process is done
recursively until it finds a frequent node. FP-Tree algorithm
does the mining process without generating candidate
itemsets. Original database is stored in a tree form, and then
the mining process is done one by one for all frequent items
in database. Those three algorithms have their own benefits
and weaknesses. Eclat and Top-Down algorithm have their
advantages in making branch of the tree. Eclat makes its
branch by checking whether that combination is already in
database or not. On other hand, Top-Down makes its
branch by looking whether the branch is already made by
another node or not. FP-Tree algorithm’s advantage is it
can generate a little bit candidate itemsets unlike apriori.
The weakness of Eclat and Top-Down is in the
characteristic of the database. If the database has a lot of
frequent items and the minimum support value is small, the
combination process will waste the time. The weakness of
FP-Tree is it saves the original database in a tree form, so it
spends a lot of memory.
Keywords-data mining; association rule mining; Eclat;
FP-Tree; Top-Down;
I.
INTRODUCTION
Data mining has provided a huge contribution in the
development of various disciplines. Data mining aims to
find a pattern of a lot of data collection, and has an
important role in corporate decision-making that has a
large-scale work. Corporate transaction data is processed
to discover patterns from these data. Before the pattern is
found, the data must take several steps, such as data
selection, data cleaning and preprocessing, data
transformation, data reduction (elimination of unnecessary
data), data mining task and algorithm selection , postprocessing, and the last is to apply the techniques in data
mining.
One of the tasks of data mining is to discover frequent
item sets. Frequent item sets is pairs of data that often
arise. Frequent itemset has important role in data mining
that in the application to find specific patterns in the
database, such as association rule, correlation, sequence,
276
episode, classifier, and cluster. Of all these patterns, one
of the highlights is the association rule. Association rule
based on searching of motivation to do an analysis of
transactions in supermarkets. From the analysis of these
transactions can studied about customer behavior in
shopping. Association rule produces conclusions about
how often a product is purchased in conjunction with
other products.
Data set
L-Itemsets Search
Algorithms
Large
Itemsets
Generate
Association Rule
Association
Rule
Figure 1. Association Rule Mining Process
Figure 1 shows that the data mining process starts
from the preparation of data sets. Then the filtered data is
brought to the next process that is selection of appropriate
algorithms to search Litemset (Large Itemset).
There are various algorithms that can be used to
discovery of large litemset. But in this study only Eclat
algorithm, FP-Tree, and Top-Down will be discussed.
II.
RELATED THEORIES
A. FP-Tree Algorithm
In this chapter will discuss about searching frequent
pattern using the Frequent Pattern Tree (FP-Tree)
algorithm. Input from the FP-Tree algorithm consists of
two fields, which is the index field and target fields.
In this case, field index only use to sort the data. In the
FP-Tree algorithm, there is no candidate generation
process. This is caused by the formation of candidate
generation is a factor that causes delays. Large Itemset
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Yogyakarta, 2 - 3 March 2010
Proceedings of ICGC-RCICT 2010
Technical Paper
search process by using the FP-Tree algorithm can be seen
in figure 2.
Data set
number of frequencies. If the frequent items are
sorted by frequency of occurrence desendingly the
possibility of the emergence of the same prefix
string will be greater.
2) Example of Tree Formation
The following will be given a transaction database,
database is to clarify the construction of the FP-Tree. The
database contains information of transaction id and item
purchased as shown by the first two columns of table 1
and a minimum support İ .
Tree Construction
3:1
FP-Tree
The first step performed in FP-Tree algorithm, which
is scanning the database to get the whole first frequent
itemset and record the frequency of each item and it
compared with the minimum support. If the frequency of
itemset is below the minimum support then the itemset
will not be used in next process.
Conditional Pattern
Search
3:3
Conditional
Pattern
Frequent itemset that has been obtained then sorted
descendingly based on their frequency. The next step is to
form the root of the tree is initialized with NIL. Database
then scanned for the second time. Sequencing performed
on the database on each transaction. The items on each
transaction are sorted according to items name that are
ordered based on the results of the previous process.
Sequencing results in a database transaction can be seen in
Table 1.
FP-Growth
3:4
Large
Itemsets
Figure 2. Large Itemset Search Process with FP-Tree
FP-Tree algorithm is an algorithm for mining the
database which this algorithm minimizes the candidate
search process. Candidate is search in the FP-Tree which
is only often appears. Otherwise, FP-Tree use tree
structure so it can avoid reading the database repeatedly.
This is certainly different from Apriori algorithm that
doing readings database continuously. Apriori algorithm
is waste time.
1) Tree Construction
Data structure on FP-Tree must be right so that it can
accommodate all the information required in the mining
process accurately. Making data structures in the FP-Tree
should be drawn up based on some facts that made
observations as follows:
x
Database search must be done at one time only.
Search this database must be able to classify the
items that included in the frequent itemset. The
frequency of appearance of these items should
also be stored in addition to the name of the item
itself. This is done because the mining process is
only done on frequent item only. Frequent items
will be removed.
x
If the structure of frequent items storage of each
transaction is right then search the database
repeatedly can be avoided.
x
Transactions that have set of the same frequent
item can be merged into one and only need to
increase the frequency. To be able to find two sets
of item are same or not, each transaction must be
ordered either in ascending or descending.
x
In two transactions that have the same common
prefix based on a particular item frequent
sequences, the same parts can be combined into a
single structure with the same prefix include the
Yogyakarta, 2 - 3 March 2010
ISSN: 2086-4868
TABLE I.
TID
EXAMPLE OF TRANSACTION DATABASE
Items
Bought
Frequency (ordered)
100
f; a; c; d; g; i; m; p;
f; c; a; m; p;
200
a; b; c; f; l; m; o;
f; c; a; b; m;
300
b; f; h; j; o;
f; b;
400
b; c; k; s; p;
c; b; p;
500
a; f; c; e; l; p; m; n;
f; c; a; m; p;
After reading the database for the second time, then do
the reading of the transaction. First of all, the first
transactions will be read, so will get the set of frequent
items (f, c, a, m, p). In the first transaction, all of the
frequent items always inserted as a new node into the tree
and it will be forming the first path tree. For the first time
link for each node is always null. Next, all of frequent
items wil bee inserted in header table. Item name inserted
in the item column, the number of frequent items inserted
in a count column, and the head links column pointing to
each formed node. In the first transaction, the count value
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Figure 3. First Step of FP-Tree
277
ISSN: 2086-4868
Technical Paper
of all rows equal to one. Figure of FP-Tree formation of
the first transaction can be seen in figure 3. Process
conducted in the same way until the fifth transaction.
3) Mining Process with FP-Tree
Generally, the process of mining using FP-Tree can be
divided into 4 steps. The steps are intended as follows:
1.
Mining in the FP-Tree starts from an item with
the smallest support in the header table.
2.
Find frequent items that contain items that will be
mining.
3.
Look k- conditional pattern base for k items that
will be mining.
4.
Form of conditional FP-Tree based on the
conditional pattern base that has been formed
previously.
Proceedings of ICGC-RCICT 2010
B. Eclat Algorithm
Eclat algorithm was first introduced by Mohammed
Javeed Zaki in 1997. The emergence of this algorithm
must not be separated from the Apriori. In the Eclat
algorithm does not perform database read repeatedly as in
the Apriori algorithm.
Input for Eclat algorithm consists of two fields, index
field and target fields. Large Itemset search process by
using the Eclat algorithm can be seen in figure 4.
Data set
Search and Sort
Frequent Item
4) Conditional Pattern Base Search
After making the formation of the tree that has been
given before which is based on mining step in the FPTree, the next thing to do is do a search conditional
pattern base of each item that has been listed in the case
example of table 1. The following example is to mining
on the p-conditional pattern base.
The first mining in a given case starting from the node
p. What to do is look at the header table, head-link from
node p starts from where and to where. Based on the
observations obtained frequent patterns containing p,
which is (f: 4, c: 3, a: 3, m: 2, p: 2) and (c: 1, b: 1, p: 1). In
the first path is shown that the path (f, c, a, m, p) appears
twice in the database. Path (f, c, a) appears three times and
the path (f) itself appears four times but only appeared
twice together with the p. Therefore, the calculated path
prefix is (f: 2, c: 2, a: 2, m: 2, p: 2).
While the path (c: 1, b: 1, p: 1), the path prefix used is
(c: 1, b: 1). Of the two prefix paths will be formed p’ sub
pattern base termed p’ conditional pattern base.
Definition of p' conditional pattern base is a sub-pattern
that occurs with the following conditions of p.
From the prefix path obtained for node p, then
performed the counting support of the items contained in
the prefix path. The results support the calculation of each
item can be seen in Table 2.
TABLE II.
P-CONDITIONAL PATTERN BASE
Item
Amount
f
2
c
3
a
2
m
2
b
1
Previously been known that the minimum support
from the example of table 1 is 3. So from Table 2 can be
seen that the eligible item is item c so that item c only the
frequent items. Next is the formation of p conditional FPTree. From table 2 notice that only items c that qualifying
the requirements, then p-conditional FP-Tree consists of
only one branch only.
278
Frequent
Item
Combination Process
for each Frequent
Item
Large
Itemsets
Figure 4. Large Itemset Search Process with Eclat
1) Mining Process with Eclat
Mining process using the Eclat algorithm is basically
done by taking slices among the items that qualifying the
minimum support value. Operation frequent wedge
between these items will certainly accelerate the process
of mining by using the Eclat algorithm. If the frequent
itemset is more longer then the number of slices
operations performed will be less, so join process will be
fewer as well.
Eclat algorithm does not require complex hash-tree
structure. Therefore, the memory used by Eclat algorithm
is not too much. Even Eclat algorithm has the advantage
to allocate memory properly. It is because of the Eclat
algorithm only needs to be done with a linear scan of the
itemset. In the Eclat algorithm, the smaller value of
minimum support will found the faster frequent itemset.
As a result, mining process is also done faster. This will
be very beneficial because it can reduce operational costs.
C. Top-Down Algorithm
In the previous chapter has discussed about the FPTree and Eclat algorithm. In this chapter will discuss the
final algorithm, the Top-Down algorithm. Discussion of
Top-Down algorithm will contain about how to obtain
mining frequent itemset of the database provided. Large
Itemset search process by using Top-Down algorithm can
be seen in figure 5.
In the Top-Down algorithm, database mining starts
from the maximal itemset. Maximal itemset is the itemset
which the itemset is not part of another itemset. First, the
items search in the database that is qualifying the
minimum support. All frequent items produced will be
Dept. of EE and IT, GMU
Yogyakarta, 2 - 3 March 2010
Proceedings of ICGC-RCICT 2010
Technical Paper
merged into one and begin the process of combination of
items one by one.
Data set
Group and Search
Frequent Item
Combination Process
of Maximal Itemsets
Large
Itemsets
Figure 5. Large Itemset Search Process with Top-Down
1) Mining Process with Top-Down
Mining process by using Top-Down algorithm is
basically done by combining of the mining itemset.
Combination process is meant to remove an item in
itemset interchangeably. New itemset results from the
combination will be mining again repeatedly until
discovered frequent itemset or a search had reached the
last level.
The principle to run Top-Down algorithm is mining
process is done recursively until it was found that the
nodes that are traced are frequent. After getting the
frequent items, the next step is to form probability
combination. New nodes are automatically generated as
frequent itemset. The principle of Top-Down algorithm is
very efficient. This is based on the fact that at the time of
the formation of combinations, Top-Down algorithm
always check on the other branches that if the
combination has been previously established or not.
TESTING AND RESULT
In this chapter will be given a comparison of time and
memory used at the time of mining with Eclat, FP-Tree,
and Top-Down algorithms. Mining for testing program
will use several databases with different support value. So
at each mining on a database will be given some support
value by using the three algorithms.
The process of testing conducted in this study using a
computer with Pentium processor 4IV 1.8 GHz and 256
Mb of memory. The operating system used in this testing
process is Microsoft Windows 2000. Database that will be
used for testing is BMS-WebView-1 database which has
total of 22,225 transactions, and using Movie database
that has total of 5888 transactions.
In this chapter will be divided into two sub-chapters
which the first section will explain about the time
comparison and the second section will explain about the
memory comparison that is used by the three algorithms.
Database that used is Microsoft Access database.
Yogyakarta, 2 - 3 March 2010
A. Time Comparison
Time used to perform mining will vary for each
algorithms. In some case of certain databases may Eclat
algorithm has faster mining times than FP-Tree algorithm
or on other database mining process with Top-Down
algorithm faster than another algorithm. Mining time is
also influenced by how much support is used.
TABLE III.
Maximal
Itemsets
III.
ISSN: 2086-4868
USING BMS-WEBVIEW-1 DATABASE (HH:MM:SS)
Algorithms
Min.
Support
Eclat
FP-Tree
Top-Down
0.02
00:00:53
00:01:49
00:02:43
0.03
00:00:33
00:01:40
00:54:39
0.04
00:00:24
00:01:48
00:00:34
0.05
00:00:12
00:01:55
00:00:01
In table 3 it appears that Top-Down algorithm has the
fastest time compared with Eclat and FP-Tree if the
minimum support that used are 0.04 and 0.05. With that
support, Eclat algorithm when compared with the FP-Tree
has better time mining. However, the smaller of the
support given in the BMS-WebView-1 database mining,
the time mining with Top-Down algorithm worse than
other algorithms. FP-Tree algorithm in this case has a
lapse of time that was stable which is compared with other
algorithms.
TABLE IV.
USING MOVIE DATABASE (HH:MM:SS)
Algorithms
Min.
Support
Eclat
FP-Tree
Top-Down
0.03
00:00:30
00:01:24
12:11:05
0.04
00:00:10
00:00:51
02:41:54
0.05
00:00:02
00:00:09
00:00:00
In table 4, the minimum support used in the
experiments using movie database is 0.3, 0.4, and 0.5.
When use support 0.5, Top-Down algorithm has the
fastest time mining compared to other algorithms.
However, when the minimum support is reduced, the
time mining required for mining with Top-Down become
more greater. In the case of movie database, Eclat
algorithm has small time mining and stable when
compared with FP-Tree and Top-Down algorithm.
B. Memory Comparison
Eclat, FP-Tree, and Top-Down algorithm has
similarity in mining process, the algorithms are the same
conduct tree formation which will be used in the database
mining. The formation of the tree will use the available
memory in computers. This section will discuss about the
comparison of memory used in the mining process using
the Eclat, FP-Tree, and Top-Down algorithm.
Table 5 shows that in experiments using FP-Tree
algorithm requires more memory than Eclat and TopDown algorithm. At first, Top-Down algorithm using the
most memory-efficient than other algorithms (support =
0.05 and support = 0.04), but when support is smaller then
the amount of memory required is greater. Eclat algorithm
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279
ISSN: 2086-4868
Technical Paper
has a change in the amount of memory was stable
between the three algorithms.
TABLE V.
USING BMS-WEBVIEW-1 DATABASE
[2]
[3]
Algorithms
Min.
Support
Eclat
FP-Tree
Top-Down
0.02
0.2M
7.3M
3.8M
0.03
0.07M
5.5M
2.6M
0.04
0.05M
5.0M
0.04M
0.05
0.02M
4.6M
0.002M
[4]
[5]
[6]
TABLE VI.
[7]
USING MOVIE DATABASE
Algorithms
Min.
Support
Eclat
FP-Tree
Top-Down
0.03
0.02M
10.0M
20.0M
0.04
0.007M
6.3M
4.7M
0.05
0.002M
1.9M
0.0008M
[8]
Proceedings of ICGC-RCICT 2010
R. Agrawal, R. Srikant, “Mining sequential patterns:
Generalization and performance improvements”, In Proceedings
1st International Conference Knowledge Discovery and Data
Mining, 1993.
C. Borgelt, “Efficient Implementation of Apriori and Eclat”,
Unpublised, Magdeburg: Department of Knowledge Processing
and Language Engineering School of Computer Science, Ottovon-Guericke-University of Magdeburg.
B. Goethals, “Memory Issues in Frequent Itemset Mining”,
Unpublised, Helsinki: Department of Computer Science,
University of Helsinki.
J. Han, J. Pei, Y. Yin, “Mining Frequent Pattern Without
Candidate Generation”, Unpublised, School of Computer Science,
Simon Fraser University.
J. Han, M. Kamber, “Data Mining: Concepts and Techniques”,
1999.
S. Parthasarathy, M.J. Zaki, M. Ogihara, W. Li, “New Algorithms
for Fast Discovery of Association Rules”, New York: Computer
Science Department, University of Rochester, July 1997.
M.J. Zaki, “Scalable Data Mining for Rules”, New York:
Department of Computer Science, University of Rochester, 1998.
In table 6 can be seen that, the process of mining by
using the Eclat algorithm was needed less amount of
memory when compared with other algorithms.
Meanwhile, at a minimum the use of support 0.4 and 0.5,
it need larger memory when using the Top-Down
algorithm and so did the difference amount of memory
required when using support 0.3 and 0.4.
IV.
CONCLUSION
In making this study, authors obtain some conclusions
include:
x
Disappearance process of unfrequent items makes
FPTree formation faster. Elimination of items
produce a new table that contains frequent items
that will accelerate the formation of tree. The
formation of tree no longer access the original
database, but only access the new table.
x
FP-Tree has a fast time mining even use larger
size dataset. While lack of FP-Tree is this
algorithm takes a large memory.
x
Top-Down algorithm does not need to do pruning
like FP-Tree. Top-Down weakness is when the
type of items and lots of frequent items and the
minimum of the support will waste time because
Top-Down continue to search until it found
frequent itemset.
x
Branch formation by using the Eclat algorithm
always look in advance whether the combination
is contained in the database or not. This will avoid
the formation of branches that are not needed. The
weakness is the algorithm always doing support
calculations despite the itemset is already frequent
because this algorithm will stop if found
unfrequent itemset.
REFERENCES
[1]
280
R. Agrawal, R. Srikant, “Fast Algorithms for Mining Association
Rules”, San Jose: IBM Almaden Research Centre.
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