A Matlab GUI Package for Comparing Data Clustering Algorithms

Abstract

The result of one clustering algorithm can be very different from that of another for the same input dataset as the other input parameters of an algorithm can substantially affect the behavior and execution of the algorithm. Cluster validity indices measure the goodness of a clustering solution. Cluster validation is very important issue in clustering analysis because the result of clustering needs to be validated in most applications. In most clustering algorithms, the number of clusters is set as a user parameter. There are a number of approaches to find the best number of clusters. Validity measures can be used to find the partitioning that best fits the underlying data (to find how good the clustering is). This chapter describes an application (CLUSTER) developed in the Matlab/GUI environment that represents an interface between the user and the results of various clustering algorithms. The user selects algorithm, internal validity index, external validity index, number of clusters, number of iterations etc. from the active windows. In this Package we compare the results of k-means, fuzzy c-means, hierarchical clustering and multiobjective clustering with support vector machine (MocSvm). This chapter presents a MATLAB Graphical User Interface (GUI) that allows the user to easily “find” the goodness of a clustering solution and immediately see the difference of those algorithms graphically. Matlab (R2008a) Graphical User Interface is used to implement this application package.