A Robust Fuzzy Approach For Gene Expression Data Clustering

Abstract In the big data era, clustering is one of the most popular data mining method. The majority of clustering algorithms have complications like automatic cluster number determination, poor clustering precision, inconsistent clustering of various datasets and parameter-dependent etc. A new fuzzy autonomous solution for clustering named Meskat-Mahmudul (MM) clustering algorithm proposed to overcome the complexity of parameter–free automatic cluster number determination and clustering accuracy. MM clustering algorithm finds out the exact number of clusters based on Average Silhouette method in multivariate mixed attribute dataset, including real-time gene expression dataset and dealt missing values, noise and outliers. MM Extended K-Means (MMK) clustering algorithm is an enhancement of the K-Means algorithm, which serves the purpose for automatic cluster discovery and runtime cluster placement. Several validation methods used to evaluate cluster and certify optimum cluster partitioning and perfection. Some datasets used to assess the performance of the proposed algorithms to other algorithms in terms of time complexity and clustering efficiency. Finally, MM clustering and MMK clustering algorithms found superior over conventional algorithms.