Fuzzy based clustering method on yeast dataset with different fuzzification methods

Abstract

Clustering is a process for classifying objects or patterns in such a way that samples of the same group are more similar to one another than samples belonging to different groups. In this paper, we introduce the clustering method called soft clustering and its type Fuzzy C-Means. The clustering algorithms are improved by implementing the two different membership functions. The Fuzzy C-Means algorithm can be improved by implementing the Fuzzification parameter values from 1.25 to 2.0 and compared with different datasets using Davis Bouldin Index. The Fuzzification parameter 2.0 is most suitable for Fuzzy C-Means clustering algorithm than other Fuzzification parameter. The Fuzzy C-Means and K-Means clustering algorithms are implemented and executed in Matlab and compared with Execution speed and Iteration Count Methods. The Fuzzy C-Means clustering method achieve better results and obtain minimum DB index for all the different cluster values from different datasets. The experimental results shows that the Fuzzy C-Means method performs well when compare with the K-Means clustering.