Open Access Open Access  Restricted Access Subscription or Fee Access

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

S. Senthil, R. David Chandrakumar


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.


Euclidean minimum spanning tree, eccentricity, standard deviation, Gaussian function, Fuzzy membership function, Fuzzy clustering.

Full Text:


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information. Also: DOI is paid service which provided by a third party. We never mentioned that we go for this for our any journal. However, journal have no objection if author go directly for this paid DOI service.