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.
Keywords: Hard clustering, K-means clustering, Hierarchical clustering, Fuzzy clustering, EST, Fuzzy C-means Clustering
How to Cite?: Abhilasha Chaudhuri, Asha Ambhaikar, "Performance Comparison of Hard and Fuzzy Clustering Algorithms on ESTs of Human Genes", Volume 3 Issue 6, June 2014, International Journal of Science and Research (IJSR), Pages: 1634-1638, https://www.ijsr.net/getabstract.php?paperid=2014564, DOI: https://dx.doi.org/10.21275/2014564