Estimating the number of clusters via system evolution for cluster analysis of gene expression data

Authors:
Kaijun Wang;Jie Zheng;Junying Zhang;Jiyang Dong
Affiliations:
School of Mathematics and Computer Science, Fujian Normal University, Fuzhou and School of Computer Science and Technology, Xidian University, Xi'an, China;Department of Physiotherapy and Acupuncture, Hospital of Fujian Normal University, Fuzhou, China;School of Computer Science and Technology, Xidian University, Xi'an, China;Department of Physics, Xiamen University, Xiamen, China
Venue:
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Year:
2009

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Abstract

The estimation of the number of clusters (NC) is one of crucial problems in the cluster analysis of gene expression data. Most approaches available give their answers without the intuitive information about separable degrees between clusters. However, this information is useful for understanding cluster structures. To provide this information,we propose system evolution (SE) method to estimate NC based on partitioning around medoids (PAM) clustering algorithm. SE analyzes cluster structures of a dataset from the view point of a pseudothermodynamics system. The system will go to its stable equilibrium state, at which the optimal NC is found, via its partitioning process and merging process. The experimental results on simulated and real gene expression data demonstrate that the SE works well on the data with well-separated clusters and the one with slightly overlapping clusters.