Approaches to Other Data Mining Problems
28.5 Approaches to Other Data Mining Problems
28.5.1 Discovery of Sequential Patterns
The discovery of sequential patterns is based on the concept of a sequence of item- sets. We assume that transactions such as the supermarket-basket transactions we discussed previously are ordered by time of purchase. That ordering yields a sequence of itemsets. For example, {milk, bread, juice}, {bread, eggs}, {cookies, milk, coffee} may be such a sequence of itemsets based on three visits by the same cus-
tomer to the store. The support for a sequence S of itemsets is the percentage of the given set U of sequences of which S is a subsequence. In this example, {milk, bread, juice} {bread, eggs} and {bread, eggs} {cookies, milk, coffee} are considered subsequences . The problem of identifying sequential patterns, then, is to find all subsequences from the given sets of sequences that have a user-defined minimum
support. The sequence S 1 ,S 2 ,S 3 , ... is a predictor of the fact that a customer who buys itemset S 1 is likely to buy itemset S 2 and then S 3 , and so on. This prediction is based on the frequency (support) of this sequence in the past. Various algorithms have been investigated for sequence detection.
28.5.2 Discovery of Patterns in Time Series
Time series are sequences of events; each event may be a given fixed type of a trans- action. For example, the closing price of a stock or a fund is an event that occurs every weekday for each stock and fund. The sequence of these values per stock or fund constitutes a time series. For a time series, one may look for a variety of pat- terns by analyzing sequences and subsequences as we did above. For example, we might find the period during which the stock rose or held steady for n days, or we might find the longest period over which the stock had a fluctuation of no more than 1 percent over the previous closing price, or we might find the quarter during which the stock had the most percentage gain or percentage loss. Time series may be compared by establishing measures of similarity to identify companies whose stocks behave in a similar fashion. Analysis and mining of time series is an extended func- tionality of temporal data management (see Chapter 26).
28.5.3 Regression
Regression is a special application of the classification rule. If a classification rule is regarded as a function over the variables that maps these variables into a target class variable, the rule is called a regression rule. A general application of regression occurs when, instead of mapping a tuple of data from a relation to a specific class, the value of a variable is predicted based on that tuple. For example, consider a relation
LAB_TESTS (patient ID, test 1, test 2, ..., test n)
1058 Chapter 28 Data Mining Concepts
which contains values that are results from a series of n tests for one patient. The target variable that we wish to predict is P, the probability of survival of the patient. Then the rule for regression takes the form:
(test 1 in range 1 ) and (test 2 in range 2 ) and ... (test n in range n ) ⇒ P = x,
or x < P ≤y The choice depends on whether we can predict a unique value of P or a range of val-
ues for P. If we regard P as a function:
P = f (test 1, test 2, ..., test n) the function is called a regression function to predict P. In general, if the function
appears as
Y=f( X1 , X2 , ..., Xn ) ,
and f is linear in the domain variables x i , the process of deriving f from a given set of tuples for < X1 , X2 , ..., Xn , y> is called linear regression. Linear regression is a com- monly used statistical technique for fitting a set of observations or points in n dimensions with the target variable y.
Regression analysis is a very common tool for analysis of data in many research domains. The discovery of the function to predict the target variable is equivalent to
a data mining operation.
28.5.4 Neural Networks
A neural network is a technique derived from artificial intelligence research that uses generalized regression and provides an iterative method to carry it out. Neural networks use the curve-fitting approach to infer a function from a set of samples. This technique provides a learning approach; it is driven by a test sample that is used for the initial inference and learning. With this kind of learning method, responses to new inputs may be able to be interpolated from the known samples. This interpo- lation, however, depends on the world model (internal representation of the prob- lem domain) developed by the learning method.
Neural networks can be broadly classified into two categories: supervised and unsu- pervised networks. Adaptive methods that attempt to reduce the output error are supervised learning methods, whereas those that develop internal representations without sample outputs are called unsupervised learning methods.
Neural networks self-adapt; that is, they learn from information about a specific problem. They perform well on classification tasks and are therefore useful in data mining. Yet, they are not without problems. Although they learn, they do not pro- vide a good representation of what they have learned. Their outputs are highly quantitative and not easy to understand. As another limitation, the internal repre- sentations developed by neural networks are not unique. Also, in general, neural networks have trouble modeling time series data. Despite these shortcomings, they
28.5 Approaches to Other Data Mining Problems 1059
28.5.5 Genetic Algorithms
Genetic algorithms (GAs) are a class of randomized search procedures capable of adaptive and robust search over a wide range of search space topologies. Modeled after the adaptive emergence of biological species from evolutionary mechanisms,
and introduced by Holland, 6 GAs have been successfully applied in such diverse fields as image analysis, scheduling, and engineering design.
Genetic algorithms extend the idea from human genetics of the four-letter alphabet (based on the A,C,T,G nucleotides) of the human DNA code. The construction of a genetic algorithm involves devising an alphabet that encodes the solutions to the decision problem in terms of strings of that alphabet. Strings are equivalent to indi- viduals. A fitness function defines which solutions can survive and which cannot. The ways in which solutions can be combined are patterned after the cross-over operation of cutting and combining strings from a father and a mother. An initial population of a well-varied population is provided, and a game of evolution is played in which mutations occur among strings. They combine to produce a new generation of individuals; the fittest individuals survive and mutate until a family of successful solutions develops.
The solutions produced by GAs are distinguished from most other search tech- niques by the following characteristics:
A GA search uses a set of solutions during each generation rather than a sin- gle solution.
The search in the string-space represents a much larger parallel search in the space of encoded solutions.
The memory of the search done is represented solely by the set of solutions available for a generation.
A genetic algorithm is a randomized algorithm since search mechanisms use probabilistic operators.
While progressing from one generation to the next, a GA finds near-optimal balance between knowledge acquisition and exploitation by manipulating encoded solutions.
Genetic algorithms are used for problem solving and clustering problems. Their ability to solve problems in parallel provides a powerful tool for data mining. The drawbacks of GAs include the large overproduction of individual solutions, the ran- dom character of the searching process, and the high demand on computer process- ing. In general, substantial computing power is required to achieve anything of significance with genetic algorithms.
6 Holland’s seminal work (1975) entitled Adaptation in Natural and Artificial Systems introduced the idea
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Parts
» Fundamentals_of_Database_Systems,_6th_Edition
» Characteristics of the Database Approach
» Advantages of Using the DBMS Approach
» A Brief History of Database Applications
» Schemas, Instances, and Database State
» The Three-Schema Architecture
» The Database System Environment
» Centralized and Client/Server Architectures for DBMSs
» Classification of Database Management Systems
» Domains, Attributes, Tuples, and Relations
» Key Constraints and Constraints on NULL Values
» Relational Databases and Relational Database Schemas
» Integrity, Referential Integrity, and Foreign Keys
» Update Operations, Transactions, and Dealing with Constraint Violations
» SQL Data Definition and Data Types
» Specifying Constraints in SQL
» The SELECT-FROM-WHERE Structure of Basic SQL Queries
» Ambiguous Attribute Names, Aliasing, Renaming, and Tuple Variables
» Substring Pattern Matching and Arithmetic Operators
» INSERT, DELETE, and UPDATE Statements in SQL
» Comparisons Involving NULL and Three-Valued Logic
» Nested Queries, Tuples, and Set/Multiset Comparisons
» The EXISTS and UNIQUE Functions in SQL
» Joined Tables in SQL and Outer Joins
» Grouping: The GROUP BY and HAVING Clauses
» Discussion and Summary of SQL Queries
» Specifying General Constraints as Assertions in SQL
» Introduction to Triggers in SQL
» Specification of Views in SQL
» View Implementation, View Update, and Inline Views
» Schema Change Statements in SQL
» Sequences of Operations and the RENAME Operation
» The UNION, INTERSECTION, and MINUS Operations
» The CARTESIAN PRODUCT (CROSS PRODUCT) Operation
» Variations of JOIN: The EQUIJOIN and NATURAL JOIN
» Additional Relational Operations
» Examples of Queries in Relational Algebra
» The Tuple Relational Calculus
» The Domain Relational Calculus
» Using High-Level Conceptual Data Models
» Entity Types, Entity Sets, Keys, and Value Sets
» Relationship Types, Relationship Sets, Roles, and Structural Constraints
» ER Diagrams, Naming Conventions, and Design Issues
» Example of Other Notation: UML Class Diagrams
» Relationship Types of Degree Higher than Two
» Subclasses, Superclasses, and Inheritance
» Constraints on Specialization and Generalization
» Specialization and Generalization Hierarchies
» Modeling of UNION Types Using Categories
» A Sample UNIVERSITY EER Schema, Design Choices, and Formal Definitions
» Data Abstraction, Knowledge Representation, and Ontology Concepts
» ER-to-Relational Mapping Algorithm
» Discussion and Summary of Mapping for ER Model Constructs
» Mapping EER Model Constructs
» The Role of Information Systems
» The Database Design and Implementation Process
» Use of UML Diagrams as an Aid to Database Design Specification 6
» Rational Rose: A UML-Based Design Tool
» Automated Database Design Tools
» Introduction to Object-Oriented Concepts and Features
» Object Identity, and Objects versus Literals
» Complex Type Structures for Objects and Literals
» Encapsulation of Operations and Persistence of Objects
» Type Hierarchies and Inheritance
» Other Object-Oriented Concepts
» Object-Relational Features: Object Database Extensions to SQL
» Overview of the Object Model of ODMG
» Built-in Interfaces and Classes in the Object Model
» Atomic (User-Defined) Objects
» Extents, Keys, and Factory Objects
» The Object Definition Language ODL
» Differences between Conceptual Design of ODB and RDB
» Mapping an EER Schema to an ODB Schema
» Query Results and Path Expressions
» Overview of the C++ Language Binding in the ODMG Standard
» Structured, Semistructured, and Unstructured Data
» XML Hierarchical (Tree) Data Model
» Well-Formed and Valid XML Documents and XML DTD
» XPath: Specifying Path Expressions in XML
» XQuery: Specifying Queries in XML
» Extracting XML Documents from
» Database Programming: Techniques
» Retrieving Single Tuples with Embedded SQL
» Retrieving Multiple Tuples with Embedded SQL Using Cursors
» Specifying Queries at Runtime Using Dynamic SQL
» SQLJ: Embedding SQL Commands in Java
» Retrieving Multiple Tuples in SQLJ Using Iterators
» Database Programming with SQL/CLI Using C
» JDBC: SQL Function Calls for Java Programming
» Database Stored Procedures and SQL/PSM
» PHP Variables, Data Types, and Programming Constructs
» Overview of PHP Database Programming
» Imparting Clear Semantics to Attributes in Relations
» Redundant Information in Tuples and Update Anomalies
» Normal Forms Based on Primary Keys
» General Definitions of Second and Third Normal Forms
» Multivalued Dependency and Fourth Normal Form
» Join Dependencies and Fifth Normal Form
» Inference Rules for Functional Dependencies
» Minimal Sets of Functional Dependencies
» Properties of Relational Decompositions
» Dependency-Preserving Decomposition
» Dependency-Preserving and Nonadditive (Lossless) Join Decomposition into 3NF Schemas
» Problems with NULL Values and Dangling Tuples
» Discussion of Normalization Algorithms and Alternative Relational Designs
» Further Discussion of Multivalued Dependencies and 4NF
» Other Dependencies and Normal Forms
» Memory Hierarchies and Storage Devices
» Hardware Description of Disk Devices
» Magnetic Tape Storage Devices
» Placing File Records on Disk
» Files of Unordered Records (Heap Files)
» Files of Ordered Records (Sorted Files)
» External Hashing for Disk Files
» Hashing Techniques That Allow Dynamic File Expansion
» Other Primary File Organizations
» Parallelizing Disk Access Using RAID Technology
» Types of Single-Level Ordered Indexes
» Some General Issues Concerning Indexing
» Algorithms for External Sorting
» Implementing the SELECT Operation
» Implementing the JOIN Operation
» Algorithms for PROJECT and Set
» Notation for Query Trees and Query Graphs
» Heuristic Optimization of Query Trees
» Catalog Information Used in Cost Functions
» Examples of Cost Functions for SELECT
» Examples of Cost Functions for JOIN
» Example to Illustrate Cost-Based Query Optimization
» Factors That Influence Physical Database Design
» Physical Database Design Decisions
» An Overview of Database Tuning in Relational Systems
» Transactions, Database Items, Read and Write Operations, and DBMS Buffers
» Why Concurrency Control Is Needed
» Transaction and System Concepts
» Desirable Properties of Transactions
» Serial, Nonserial, and Conflict-Serializable Schedules
» Testing for Conflict Serializability of a Schedule
» How Serializability Is Used for Concurrency Control
» View Equivalence and View Serializability
» Types of Locks and System Lock Tables
» Guaranteeing Serializability by Two-Phase Locking
» Dealing with Deadlock and Starvation
» Concurrency Control Based on Timestamp Ordering
» Multiversion Concurrency Control Techniques
» Validation (Optimistic) Concurrency
» Granularity of Data Items and Multiple Granularity Locking
» Using Locks for Concurrency Control in Indexes
» Other Concurrency Control Issues
» Recovery Outline and Categorization of Recovery Algorithms
» Caching (Buffering) of Disk Blocks
» Write-Ahead Logging, Steal/No-Steal, and Force/No-Force
» Transaction Rollback and Cascading Rollback
» NO-UNDO/REDO Recovery Based on Deferred Update
» Recovery Techniques Based on Immediate Update
» The ARIES Recovery Algorithm
» Recovery in Multidatabase Systems
» Introduction to Database Security Issues 1
» Discretionary Access Control Based on Granting and Revoking Privileges
» Mandatory Access Control and Role-Based Access Control for Multilevel Security
» Introduction to Statistical Database Security
» Introduction to Flow Control
» Encryption and Public Key Infrastructures
» Challenges of Database Security
» Distributed Database Concepts 1
» Types of Distributed Database Systems
» Distributed Database Architectures
» Data Replication and Allocation
» Example of Fragmentation, Allocation, and Replication
» Query Processing and Optimization in Distributed Databases
» Overview of Transaction Management in Distributed Databases
» Overview of Concurrency Control and Recovery in Distributed Databases
» Current Trends in Distributed Databases
» Distributed Databases in Oracle 13
» Generalized Model for Active Databases and Oracle Triggers
» Design and Implementation Issues for Active Databases
» Examples of Statement-Level Active Rules
» Time Representation, Calendars, and Time Dimensions
» Incorporating Time in Relational Databases Using Tuple Versioning
» Incorporating Time in Object-Oriented Databases Using Attribute Versioning
» Temporal Querying Constructs and the TSQL2 Language
» Spatial Database Concepts 24
» Multimedia Database Concepts
» Clausal Form and Horn Clauses
» Datalog Programs and Their Safety
» Evaluation of Nonrecursive Datalog Queries
» Introduction to Information Retrieval
» Types of Queries in IR Systems
» Evaluation Measures of Search Relevance
» Web Analysis and Its Relationship to Information Retrieval
» Analyzing the Link Structure of Web Pages
» Approaches to Web Content Analysis
» Trends in Information Retrieval
» Data Mining as a Part of the Knowledge
» Goals of Data Mining and Knowledge Discovery
» Types of Knowledge Discovered during Data Mining
» Market-Basket Model, Support, and Confidence
» Frequent-Pattern (FP) Tree and FP-Growth Algorithm
» Other Types of Association Rules
» Approaches to Other Data Mining Problems
» Commercial Data Mining Tools
» Data Modeling for Data Warehouses
» Difficulties of Implementing Data Warehouses
» Grouping, Aggregation, and Database Modification in QBE
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