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.
Toolbox™ tools allow you to find clusters in input-output
training data. You can use the cluster information to generate a Sugeno-type
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.
to automatically generate this type of FIS, use the
Fuzzy c-means (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  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
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,
every data point a membership grade for each cluster. By iteratively
updating the cluster centers and the membership grades for each data
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
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
help you build a fuzzy inference system by creating membership functions
to represent the fuzzy qualities of each cluster. To generate a Sugeno-type
fuzzy inference system that models the behavior of input/output data,
you can configure the
to use FCM 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 . The cluster estimates, which are obtained from the
subclust function, can be used to initialize
iterative optimization-based clustering methods (
and model identification methods (like
subclust function finds the clusters using
the subtractive clustering method.
To generate a Sugeno-type fuzzy inference system that models
the behavior of input/output data, you can configure the
genfis command to use subtractive clustering.
 Bezdec, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.
 Chiu, S., “Fuzzy Model Identification Based on Cluster Estimation,” Journal of Intelligent & Fuzzy Systems, Vol. 2, No. 3, Sept. 1994.