Title :
Finding Number of Clusters in a Gene Co-expression Network Using Independent Sets
Author_Institution :
Fac. of Coll. of Comput. Sci. & Eng., KFUPM, Dhahran, Saudi Arabia
Abstract :
Determining the number of clusters is required for most of the clustering algorithms. The number of clusters in a gene co-expression network is not known a prior. In this study, maximum independent set concept from graph theory is applied for a gene expression data set. The results indicate that employing independent set approach to approximate the number of clusters is promising.
Keywords :
biology computing; data analysis; graph theory; pattern clustering; set theory; clustering algorithms; gene co-expression network; gene expression data set; graph theory; maximum independent set concept; Approximation algorithms; Clustering algorithms; Data mining; Estimation; Gene expression; Histograms; clustering; independent sets;
Conference_Titel :
Social Computing (SocialCom), 2013 International Conference on
Conference_Location :
Alexandria, VA
DOI :
10.1109/SocialCom.2013.125
