Hybrid Fuzzy C-Means Clustering Technique for Gene Expression Data

The challenging issue in microarray technique is to analyze and interpret the large volume of data. This can be achieved by clustering techniques in data mining. In hard clustering like hierarchical and k-means clustering techniques, data is divided into distinct clusters, where each data element belongs to exactly one cluster so that the out come of the clustering may not be correct in many times. The problems addressed in hard clustering could be solved in fuzzy clustering technique. Among fuzzy based clustering, fuzzy cmeans (FCM) is the most suitable for microarray gene expression data. The problem associated with fuzzy c-means is the number of clusters to be generated for the given dataset needs to be specified in prior. This can be solved by combining this method with a popular probability related Expectation Maximization (EM) algorithm which provides the statistical frame work to model the cluster structure of gene expression data. The main objective of this proposed hybrid fuzzy c-means method is to determine the precise number of clusters and interpret the same efficiently.