Effective Minimal Spanning tree using Fuzzy C- means based on Kernel function in data analyzing

Abstract

Clustering is an exploratory data analysis tool that has gained enormous attention in the recent years specifically for gene expression data analysis. There are many data clustering techniques available to extract meaningful information from real world data. The Fuzzy C-means clustering is a method of cluster analysis which aims to partition n data points into
k clusters. This work is strongly felt that minimal spanning tree using Fuzzy C-means based kernel function is a suitable one to nd meaningful information and appropriate groups into real world datasets. In fuzzy clustering, the objective function controls the groups or clusters and computation parts of clustering.The aim of researchers in fuzzy clustering algorithm is to minimize the objective function that usually has number of computation parts, like calculation of cluster prototypes, degree of membership for objects, computation part for updating and stopping algorithms. This paper introduces some new eective objective function based standard objective function of fuzzy C-means that incorporates the robust kernel induced distance for clustering the datasets .By minimizing the novel objective function this paper obtains eective equations for optimal cluster centers and equation to achieve optimal membership grades for partitioning the given dataset. In order to solve the problems of clustering performance aected by initial centers of clusters,this paper introduces a specialized MST-clustering algorithm based center initialization method for executing the proposed algorithm in clustering datasets. We perform extensive experiments on the proposed algorithms to illustrate the effectiveness of proposed methods.