Paper Info
Title
Estimating the number of clusters via system evolution for cluster analysis of gene expression data.
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 viewpoint 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.
Year
DOI
Venue
2009
10.1109/TITB.2009.2025119
IEEE Transactions on Information Technology in Biomedicine
Keywords
Field
DocType
system evolution,pattern clustering,partitioning around medoids clustering algorithm,statistical analysis,genetics,estimation of the number of clusters,real gene expression data,intuitive information,optimal nc,se analyzes cluster structure,cluster analysis,cluster structure,partitioning around medoids,system evolution method,gene expression data,pseudothermodynamics system,bioinformatics,overlapping cluster,merging,gene expression,equilibrium state,computer science,statistics,clustering algorithms
Cluster (physics),Data mining,MATLAB,Computer science,Separable space,Stable equilibrium,Cluster analysis,Merge (version control),Medoid,Statistical analysis
Journal
Volume
Issue
ISSN
13
5
1558-0032
Citations 
PageRank 
References 
5
0.48
6
Authors
4
Name
Order
Citations
PageRank
Kaijun Wang1704.86
Jie Zheng250.48
Junying Zhang315321.12
Jiyang Dong4234.44