TY - CHAP
T1 - Improved Gene Clustering Based on Particle Swarm Optimization, K-Means, and Cluster Matching
AU - Lam, Yau King
AU - Tsang, P.W.M.
AU - Leung, Chi-Sing
N1 - Research Unit(s) information for this publication is provided by the author(s) concerned.
PY - 2011/11
Y1 - 2011/11
N2 - Past research has demonstrated that gene expression data can be effectively clustered into a group of centroids using an integration of the particle swarm optimization (PSO) and the K-Means algorithm. It is entitled PSO-based K-Means clustering algorithm (PSO-KM). This paper proposes a novel scheme of cluster matching to improve the PSO-KM for gene expression data. With the proposed scheme prior to the PSO operations, sequence of the clusters’ centroids represented in a particle is matched that of the corresponding ones in the best particle with the closest distance. On this basis, not only a particle communicates with the best one in the swarm, but also sequence of the centroids is optimized. Experimental results reflect that the performance of the proposed design is superior in term of the reduction of the clustering error and convergence rate.
AB - Past research has demonstrated that gene expression data can be effectively clustered into a group of centroids using an integration of the particle swarm optimization (PSO) and the K-Means algorithm. It is entitled PSO-based K-Means clustering algorithm (PSO-KM). This paper proposes a novel scheme of cluster matching to improve the PSO-KM for gene expression data. With the proposed scheme prior to the PSO operations, sequence of the clusters’ centroids represented in a particle is matched that of the corresponding ones in the best particle with the closest distance. On this basis, not only a particle communicates with the best one in the swarm, but also sequence of the centroids is optimized. Experimental results reflect that the performance of the proposed design is superior in term of the reduction of the clustering error and convergence rate.
KW - Gene clustering
KW - K-Means
KW - Particle Swarm Optimization (PSO)
KW - PK-Means
KW - Vector Quantization (VQ)
UR - http://www.scopus.com/inward/record.url?scp=81855226466&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-81855226466&origin=recordpage
U2 - 10.1007/978-3-642-24955-6_77
DO - 10.1007/978-3-642-24955-6_77
M3 - RGC 12 - Chapter in an edited book (Author)
SN - 9783642249549
VL - Part I
T3 - Lecture Notes in Computer Science
SP - 654
EP - 661
BT - Neural Information Processing
A2 - Lu, Bao-Liang
A2 - Zhang, Liqing
A2 - Kwok, James
PB - Springer
CY - Heidelberg
T2 - 18th International Conference on Neural Information Processing (ICONIP 2011)
Y2 - 13 November 2011 through 17 November 2011
ER -