A Novel Fuzzy Clustering Approach for Gene Classification

Abstract

Automatic cluster detection is crucial for real-time gene expression data where the quantity of missing values and noise ratio is relatively high. In this paper, algorithms of dynamical determination of the number of cluster and clustering have been proposed without any pre and post clustering assumptions. Proposed fuzzy Meskat-Hasan (MH) clustering provides solutions for sophisticated datasets. MH clustering extracts the hidden information of the unknown datasets. Based on the findings, it determines the number of clusters and performs seed based clustering dynamically. MH Extended K-Means cluster algorithm which is a nonparametric extension of the traditional K-Means algorithm and provides the solution for automatic cluster detection including runtime cluster selection. To ensure the accuracy and optimum partitioning, seven validation techniques were used for cluster evaluation. Four well known datasets were used for validation purposes. In the end, MH clustering and MH Extended K-Means clustering algorithms were found as a triumph over traditional algorithms.

A robust fuzzy approach for gene expression data clustering

“…Chen [4] developed the DC-MDACC algorithm which divides the dataset into graded dominant, numerical dominant and mixed attribute types. MH algorithm [14] also provides the solution for automatic cluster detection and identification. Jahan et al [13] shows a comparative analysis of some traditional algorithms.…”