Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brown, P., Botstein, D.: Exploring the New World of the Genome with DNA Microarrays. Nature Genetics 21, 33–37 (1999)
Brazma, A., Robinson, A., Cameron, G., Ashburner, M.: One-stop Shop for Microarray Data. Nature 403, 699–700 (2000)
Asyali, M.H., et al.: Gene Expression Profile Classification: A Review. Current Bioinformatics 1, 55–73 (2006)
Kerr, G., Ruskin, H.J., Crane, M., Doolan, P.: Techniques for clustering gene expression data. Computers in Biology and Medicine 38, 283–293 (2008)
Hartigan, J.A., Wong, M.A.: A K-Means clustering algorithm. Appl. Stat. 28, 126–130 (1979)
Du, Z., et al.: PK-Means: A new algorithm for gene clustering. Comput. Biol. Chem. 32(4), 243–247 (2008)
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69–73. IEEE Press, Piscataway (1998)
Alizadeh, A.A., et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)
Troyanskaya, O., et al.: Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525 (2001)
Spellman, P.T., et al.: Comprehensive identification of cell cycle-regulated genes of the yeast. Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9, 3273–3297 (1998)
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lam, YK., Tsang, P.W.M., Leung, CS. (2011). Improved Gene Clustering Based on Particle Swarm Optimization, K-Means, and Cluster Matching. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_77
Download citation
DOI: https://doi.org/10.1007/978-3-642-24955-6_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24954-9
Online ISBN: 978-3-642-24955-6
eBook Packages: Computer ScienceComputer Science (R0)