Abstract
Cooperative coevolution (CC) was used to improve the performance of evolutionary algorithms (EAs) on complex optimization problems in a divide-and-conquer way. In this paper, we show that the CC framework can be very helpful to improve the performance of particle swarm optimization (PSO) on clustering high-dimensional datasets. Based on CC framework, the original partitional clustering problem is first decomposed to several subproblems, each of which is then evolved by an optimizer independently. We employ a very simple but efficient optimization algorithm, namely bare-bone particle swarm optimization (BPSO), as the optimizer to solve each subproblem cooperatively. In addition, we design a new centroid-based encoding schema for each particle and apply the Chernoff bounds to decide a proper population size. The experimental results on synthetic and real-life datasets illustrate the effectiveness and efficiency of the BPSO and CC framework. The comparisons show the proposed algorithm significantly outperforms five EA-based clustering algorithms, i.e., PSO, SRPSO, ACO, ABC and DE, and K-means on most of the datasets.
Similar content being viewed by others
References
Babu G, Murty M (1994) Clustering with evolution strategies. Pattern Recognit 27(2):321–329
Bandyopadhyay S (2002) An evolutionary technique based on K-Means algorithm for optimal clustering. Inf Sci 146:221–237
van den Bergh F, Engelbrecht A (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evolut Comput 8(3):225–239
Blackwell T (2012) A study of collapse in bare bones particle swarm optimization. IEEE Trans Evolut Comput 16(3):354–372
Cura T (2012) A particle swarm optimization approach to clustering. Expert Syst Appl 39(1):1582–1588
Das S, Abraham A, Konar A (2008a) Automatic clustering using an improved differential evolution algorithm. IEEE Trans Syst Man Cybern Part A Syst Humans 38(1):218–237
Das S, Abraham A, Konar A (2008b) Automatic clustering using an improved differential evolution algorithm 38(1):218–237
Das S, Abraham A, Konar A (2008c) Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm. Pattern Recognit Lett 29(5):688–699
Frank A, Asuncion A (2010) UCI machine learning repository. http://archive.ics.uci.edu/ml
Guha S, Rastogi R, Shim K (2001) Cure: an efficient clustering algorithm for large databases. Inf Syst 26(1):35–58
Hruschka ER, Campello RJGB, Freitas AA, Ponce C, Carvalho LFD (2009) A survey of evolutionary algorithms for clustering. IEEE Trans Syst Man Cybern Part C Appl Rev 39(2):133–155
Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323
Kao YT, Zahara E, Kao IW (2008) A hybridized approach to data clustering. Expert Syst Appl 34(3):1754–1762
Kennedy J (2003) Bare bones particle swarms. In: Proceedings of the 2003 IEEE Swarm Intelligence, Symposium, pp 80–87
Krishna K, Murty N (1999) Genetic k-means algorithm. IEEE Trans Syst Man Cybern Part B Cybern 29(3):433–439
Kwedlo W (2011) A clustering method combining differential evolution with the k-means algorithm. Pattern Recognit Lett 32(12):1613–1621
Li X, Yao X (2012) Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans Evolut Comput 16(2):210–224
MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Cam LML, Neyman J (eds) Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 1. University of California Press, pp 281–297
Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recognit 33(9):1455–1465
van der Merwe D, Engelbrecht A (2003) Data clustering using particle swarm optimization. In: Proceeding of the IEEE 2003 congress on evolutionary computation, vol 1. pp 215–220
Molina D, Lozano M, Sánchez AM, Herrera F (2011) Memetic algorithms based on local search chains for large scale continuous optimisation problems: MA-SSW-Chains. Soft Comput 15(11):2201–2220. doi:10.1007/s00500-010-0647-2. http://www.springerlink.com/index/10.1007/s00500-010-0647-2
Ma PCH, Chan KCC, Yao X, Chiu DKY (2006) An evolutionary clustering algorithm for gene expression microarray data analysis. IEEE Trans Evolut Comput 10(3):296–314
Potter MA, Couldrey C (2010) A cooperative coevolutionary approach to partitional clustering. In: Proceedings of the 11th international conference on Parallel problem solving from nature: Part I, Springer, Berlin, PPSN’10, pp 374–383
Potter MA, Jong KAD (1994) A cooperative coevolutionary approach to function optimization. In: Proceedings of the third conference on parallel problem solving from nature. Springer, London, UK, PPSN III, pp 249–257
Shelokar P (2004) An ant colony approach for clustering. Anal Chimica Acta 509(2):187–195
Tsai CY, Kao IW (2011) Particle swarm optimization with selective particle regeneration for data clustering. Expert Syst Appl 38(6):6565–6576
Yang Z, Tang K, Yao X (2008) Large scale evolutionary optimization using cooperative coevolution. Inf Sci 178:2985–2999
Zhang C, Ouyang D, Ning J (2010) An artificial bee colony approach for clustering. Expert Syst Appl 37(7):4761–4767
Acknowledgments
This work is partly supported by the National Natural Science Foundation of China under Grant Nos. 61074054, 61070135 and Zhejiang Provincial Natural Science Foundation of under Grant Nos. Q13F030023 LY 13F030010 and LZ13F020002.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by G. Acampora.
Rights and permissions
About this article
Cite this article
Jiang, B., Wang, N. Cooperative bare-bone particle swarm optimization for data clustering. Soft Comput 18, 1079–1091 (2014). https://doi.org/10.1007/s00500-013-1128-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-013-1128-1