REFERENSI KUMPULAN FUZZY | User Club Subang
Fuzzy Clustering
What Is Data Clustering?
Clustering of numerical data forms the basis of many classification and system modeling algorithms. The purpose
of clustering is to identify natural groupings of data from a large data set to produce a concise representation of a system's
behavior.
Fuzzy Logic Toolbox™ tools allow you to find clusters in inputoutput training data. You can use the cluster information to
generate a Sugenotype fuzzy inference system that best models the data behavior using a minimum number of rules. The
rules partition themselves according to the fuzzy qualities associated with each of the data clusters.
Use genfis orgenfis to automatically accomplish this type of FIS generation.
Fuzzy CMeans Clustering
Fuzzy cmeans (FCM) is a data clustering technique wherein each data point belongs to a cluster to some degree that is
specified by a membership grade. This technique was originally introduced by Jim Bezdek in 1981 [1] as an improvement on
earlier clustering methods. It provides a method that shows how to group data points that populate some multidimensional
space into a specific number of different clusters.
The command line function fcm starts with an initial guess for the cluster centers, which are intended to mark the mean
location of each cluster. The initial guess for these cluster centers is most likely incorrect. Additionally, fcm assigns every data
point a membership grade for each cluster. By iteratively updating the cluster centers and the membership grades for each
data point, fcm iteratively moves the cluster centers to the right location within a data set. This iteration is based on minimizing
an objective function that represents the distance from any given data point to a cluster center weighted by that data point's
membership grade.
The command line function fcm outputs a list of cluster centers and several membership grades for each data point. You can
use the information returned by fcm to help you build a fuzzy inference system by creating membership functions to represent
the fuzzy qualities of each cluster.
The genfis function uses fcm to take inputoutput training data and generate a Sugenotype fuzzy inference system that
models the data behavior.
Subtractive Clustering
If you do not have a clear idea how many clusters there should be for a given set of data,subtractive clustering is a fast, one
pass algorithm for estimating the number of clusters and the cluster centers for a set of data [2]. The cluster estimates, which
are obtained from thesubclust function, can be used to initialize iterative optimizationbased clustering methods (fcm) and
model identification methods ( likeanfis). The subclust function finds the clusters by using the subtractive clustering
method.
The genfis function builds upon the subclust function to provide a fast, onepass method to take inputoutput training data
and generate a Sugenotype fuzzy inference system that models the data behavior.
References
[1] Bezdec, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.
[2] Chiu, S., "Fuzzy Model Identification Based on Cluster Estimation," Journal of Intelligent & Fuzzy Systems, Vol. 2, No. 3,
Sept. 1994.
See Also
fcm | subclust
Related Examples
Cluster QuasiRandom Data Using Fuzzy CMeans Clustering
Model Suburban Commuting Using Subtractive Clustering
Data Clustering Using the Clustering Tool
What Is Data Clustering?
Clustering of numerical data forms the basis of many classification and system modeling algorithms. The purpose
of clustering is to identify natural groupings of data from a large data set to produce a concise representation of a system's
behavior.
Fuzzy Logic Toolbox™ tools allow you to find clusters in inputoutput training data. You can use the cluster information to
generate a Sugenotype fuzzy inference system that best models the data behavior using a minimum number of rules. The
rules partition themselves according to the fuzzy qualities associated with each of the data clusters.
Use genfis orgenfis to automatically accomplish this type of FIS generation.
Fuzzy CMeans Clustering
Fuzzy cmeans (FCM) is a data clustering technique wherein each data point belongs to a cluster to some degree that is
specified by a membership grade. This technique was originally introduced by Jim Bezdek in 1981 [1] as an improvement on
earlier clustering methods. It provides a method that shows how to group data points that populate some multidimensional
space into a specific number of different clusters.
The command line function fcm starts with an initial guess for the cluster centers, which are intended to mark the mean
location of each cluster. The initial guess for these cluster centers is most likely incorrect. Additionally, fcm assigns every data
point a membership grade for each cluster. By iteratively updating the cluster centers and the membership grades for each
data point, fcm iteratively moves the cluster centers to the right location within a data set. This iteration is based on minimizing
an objective function that represents the distance from any given data point to a cluster center weighted by that data point's
membership grade.
The command line function fcm outputs a list of cluster centers and several membership grades for each data point. You can
use the information returned by fcm to help you build a fuzzy inference system by creating membership functions to represent
the fuzzy qualities of each cluster.
The genfis function uses fcm to take inputoutput training data and generate a Sugenotype fuzzy inference system that
models the data behavior.
Subtractive Clustering
If you do not have a clear idea how many clusters there should be for a given set of data,subtractive clustering is a fast, one
pass algorithm for estimating the number of clusters and the cluster centers for a set of data [2]. The cluster estimates, which
are obtained from thesubclust function, can be used to initialize iterative optimizationbased clustering methods (fcm) and
model identification methods ( likeanfis). The subclust function finds the clusters by using the subtractive clustering
method.
The genfis function builds upon the subclust function to provide a fast, onepass method to take inputoutput training data
and generate a Sugenotype fuzzy inference system that models the data behavior.
References
[1] Bezdec, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.
[2] Chiu, S., "Fuzzy Model Identification Based on Cluster Estimation," Journal of Intelligent & Fuzzy Systems, Vol. 2, No. 3,
Sept. 1994.
See Also
fcm | subclust
Related Examples
Cluster QuasiRandom Data Using Fuzzy CMeans Clustering
Model Suburban Commuting Using Subtractive Clustering
Data Clustering Using the Clustering Tool