Performance Comparison of Hard and Fuzzy Clustering Algorithms on ESTs of Human Genes

Abstract? In biological data analysis sequences discovered in laboratory experiments are not properly identified. Biologists attempt to group genes based on the temporal pattern of their expression levels. Clustering algorithms could provide a structure to the data. Hard clustering methods such as K-means or Hierarchical clustering assign each gene to a single cluster, whereas in fuzzy clustering methods a gene possesses varying degrees of membership with more than one cluster. Performances of both type of clustering algorithms are analyzed in this paper.