Pattern Clustering using Soft-Computing Approaches

Abstract

Clustering is the process of partitioning or grouping a given set of patterns into disjoint clusters. This is done such that patterns in the same cluster are alike and patterns belonging to two dierent clusters are dierent. . Clustering Process can be divided into two parts
Cluster formation
Cluster validation
The most trivial K-means algorithm is rst implemented on the data set obtained from UCI machine repository. The comparison is extended to Fuzzy C-means algorithm where each data is a member of every cluster but with a certain degree known as membership value. Finally, to obtain the optimal value of K Genetic K-means algorithm in implemented in which GA nds the value of K as generation evolves.The
ecieny of the three algorithms can be judged on the two measuring index such as :
the silhouette index and Davies-Bouldin Index .