Abstract
K-means is one of the most commonly used clustering methods for analyzing gene expression data, where it is sensitive to the choice of initial clustering centroids and tends to be trapped in local optima. To overcome these problems, a memetic K-means (MKMA) algorithm, which is a hybridization of particle swarm optimizer (PSO) based memetic algorithm (MA) and K-means, is proposed in this paper. In particular, the PSO based MA is used to minimize the within-cluster sum of squares and the K-means is used to iteratively fine-tune the locations of the centers. The experimental results on two gene expression datasets indicate that MKMA is capable of obtaining more compact clusters than K-means, Fuzzy K-means, and the other PSO based K-means namely PK-means. MKMA is also demonstrated to attain faster convergence rate and more robustness against the random choice of initial centroids.
This work is sponsored by National Natural Science Foundation of China (60872125 & 61001185), Program for New Century Excellent Talents in University, Fok Ying Tung Education Foundation, Guangdong Natural Science Foundation (10151806001000002) and Shenzhen City Funds for Distinguished Young Scientists.
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
Schena, M., Shalon, D., Davis, R.W., et al.: Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray. Science 270(5235), 467–470 (1995)
Milligan, G.W., Cooper, M.C.: A Study of the Comparability of External Criteria for Hierarchical Cluster Analysis. Multivariate Behavioral Research 21, 441–458 (1986)
Fritzke, B.: Growing Cell Structures-a Self-organizing Network for Unsupervised and Supervised Learning. Networks 7, 1141–1160 (1994)
MacQueen, J.B.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 271–297. University of California Press, Berkeley (1967)
Thalamuthu, A., Mukhopadhyay, I., Tseng, G.C.: Evaluation and Comparison of Gene Clustering Methods in Microarray Analysis. Bioinformatics 22(8), 2405–2412 (2006)
Gasch, A.P., Eisen, M.B.: Exploring the Conditional Coregulation of Yeast Gene Expression through Fuzzy K-means Clustering. Genome Biology 3(11), 1–22 (2002)
Du, Z.H., Wang, Y.W., Ji, Z.: PK-means:a New Algorithm for Gene Clustering. Computatoinal Biology and Chemistry 32, 243–247 (2008)
Ji, Z., Liao, H.L., Xu, W., Jiang, L.: A Strategy of Particle-pair for Vector Quantization in Image Coding. Acta Electron. Sin. 35(7), 86–89 (2007)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Network, pp. 1942–1948. IEEE Service Center, Piscataway (1995)
Moscato, P.: Memetic Algorithm: a Short Introduction. McGraw-Hill, London (1999)
Liang, J.J., Qin, A.K., et al.: Comprehensive Learing Particle Swarm Optimizer for Global Optimization of Multimodal Functions. IEEE Transactions on Evolutionary Computation 10(3), 281–295 (2006)
Spellman, P.T., et al.: Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saccharomyces Cerevisine by Microarray Hybridization. Mol. Biol. Cell 9, 3273–3297 (1998)
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Bostein, D., Altman, R.B.: Missing Value Estimation Methods for DNA Microarrays. Bioinformatics 17, 520–525 (2001)
Chu, S., et al.: The Transcriptional Program of Sporulation in Budding Yeast. Science 282, 699–705 (1998)
Alizadeh, A.A., et al.: Distinct Types of Diffuse Large B-cell Lymphoma Identified by Gene Expression Profiling. Nature 403, 503–511 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ji, Z., Liu, W., Zhu, Z. (2011). Gene Clustering Using Particle Swarm Optimizer Based Memetic Algorithm. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21515-5_69
Download citation
DOI: https://doi.org/10.1007/978-3-642-21515-5_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21514-8
Online ISBN: 978-3-642-21515-5
eBook Packages: Computer ScienceComputer Science (R0)