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Soft Computing
Published in: Soft Computing 12/2015

05-06-2015 | Focus

Hybridization of magnetic charge system search and particle swarm optimization for efficient data clustering using neighborhood search strategy

Authors: Y. Kumar, G. Sahoo

Published in: Soft Computing | Issue 12/2015

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Abstract

Clustering is a popular data analysis technique, which is applied for partitioning of datasets. The aim of clustering is to arrange the data items into clusters based on the values of their attributes. Magnetic charge system search (MCSS) algorithm is a new meta-heuristic optimization algorithm inspired by the electromagnetic theory. It has been proved better than other meta-heuristics. This paper presents a new hybrid meta-heuristic algorithm by combining both MCSS and particle swarm optimization (PSO) algorithms, which is called MCSS–PSO, for partitional clustering problem. Moreover, a neighborhood search strategy is also incorporated in this algorithm to generate more promising solutions. The performance of the proposed MCSS–PSO algorithm is tested on several benchmark datasets and its performance is compared with already existing clustering algorithms such as K-means, PSO, genetic algorithm, ant colony optimization, charge system search, chaotic charge system search algorithm, and some PSO variants. From the experimental results, it can be seen that performance of the proposed algorithm is better than the other algorithms being compared and it can be effectively used for partitional clustering problem.
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Literature
Abraham A, Das S, Roy S (2007) Swarm intelligence algorithms for data clustering. In: Soft computing for knowledge discovery and data mining, part IV. Springer, Berlin, pp 79–313
Alpaydin E (2004) Introduction to machine learning. MIT Press, Cambridge
Anderberg MR (1973) Cluster analysis for application. Academic Press, New York
Ankerst M, Breunig M, Kriegel HP, Sander J (1999) OPTICS: ordering points to identify the clustering structure. In: Proceedings of the 1999 ACM-SIGMOD international conference on management of data, Philadelphia, pp 49–60
Archer J, Robertson DL (2007) CTree: comparison of clusters between phylogenetic trees made easy. Bioinformatics 23(21):2952–2953CrossRef
Ball G, Hall D (1967) A clustering technique for summarizing multivariate data. Behav Sci 12:153–155CrossRef
Basu S, Davidson I, Wagstaff K (2008) Constrained clustering: advances in algorithms. In: Theory and applications, data mining and knowledge discovery. Chapman and Hall/CRC, London
Bezdek J (1981) Pattern recognition with fuzzy objective function algorithms. Advanced applications in pattern recognition. Plenum Press, New YorkCrossRef
Bezdek JC, Boggavarapu S, Hall LO, Bensaid A (1994) Genetic algorithm guided clustering. In: IEEE World Congress on computational intelligence and evolutionary computation, pp 34–39
Bottou L, Vapnik V (1992) Local learning algorithms. Neural Comput 4(6):888–900CrossRef
Carpenter GA, Grossberg S (1987) A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput Vis Graph Image Process 37:54–115MATHCrossRef
Celeux G, Govaert G (1995) Gaussian parsimonious clustering models. Pattern Recog 28(5):781–793CrossRef
Cheeseman P, Stutz J (1996) Bayesian classification (AutoClass): theory and results. In: Fayyard UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. AAAI/MIT Press, Cambridge
Chechik G, Globerson A, Tishby N, Weiss Y (2005) Information bottleneck for Gaussian variables. J Mach Learn Res 6:165–188MATHMathSciNet
Chen S (1995) Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning. Electron Lett 31(2):117–118CrossRef
Chi SC, Yang CC (2006) Integration of ant colony SOM and k-means for clustering analysis. In: Knowledge-based intelligent information and engineering systems. Springer, Berlin, pp 1–8
Chen CY, Ye F (2004) Particle swarm optimization algorithm and its application to clustering analysis. In: IEEE international conference on networking, sensing and control, vol 2, pp 789–794
Dalli A (2003) Adaptation of the F-measure to cluster based lexicon quality evaluation. In: Proceedings of the EACL, pp 51–60
Das S, Abraham A, Konar A (2009) Meta-heuristic clustering. Springer, Berlin
Dawyndt P, De Meyer H, De Baets B (2006) UPGMA clustering revisited: a weight-driven approach to transitive approximation. Int J Approx Reason 42(3):174–191MATHCrossRef
Day WH, Edelsbrunner H (1984) Efficient algorithms for agglomerative hierarchical clustering methods. J Classif 1(1):7–24MATHCrossRef
Demiroz G, Guvenir A (1997) Classification by voting feature intervals. In: Proceedings of the seventh european conference on machine learning, pp 85–92
Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MATHMathSciNet
Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18CrossRef
Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B: Cybern 26(1):29–41
Dunn WJ III, Greenberg MJ, Soledad SC (1976) Use of cluster analysis in the development of structure–activity relations for antitumor triazenes. J Med Chem 19(11):1299–1301CrossRef
Erol OK, Eksin I (2006) A new optimization method: big bang-big crunch. Adv Eng Softw 37(2):106–111CrossRef
Esmin AAA, Matwin S (2012) Data clustering using hybrid particle swarm optimization. In: Lecture notes in computer science, pp 159–166
Ester M, Kriegel HP, Sander J, Xu X (1996) A density based algorithm for discovering clusters in large spatial databases. In: Proceedings of the 1996 international conference on knowledge discovery and data mining (KDD’96), Portland, pp 226–231
Fraley C, Raftery AE (1999) MCLUST: software for model-based cluster analysis. J Classif 16(2):297–306MATHCrossRef
Guha S, Rastogi R, Shim K (1998) CURE: an efficient clustering algorithm for large databases. In: Proceedings of the ACM SIGMOD int. conf. management of data, pp 73–84
Guha S, Rastogi R, Shim K (2000) ROCK: a robust clustering algorithm for categorical attributes. Inf Syst 25(5):345–366CrossRef
García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf Sci 180(10):2044–2064CrossRef
Gao W, Liu SY, Huang LL (2013) A novel artificial bee colony algorithm with Powell’s method. Appl Soft Comput 13(9):3763–3775CrossRef
Handl J, Knowles J, Dorigo M (2003) On the performance of ant-based clustering. In: Design and application of hybrid intelligent system. Frontiers in artificial intelligence and applications, vol 104, pp 204–213
Hassoun MH (1995) Fundamentals of artificial neural networks. The MIT Press, Cambridge
He Y, Pan W, Jizhen L (2006) Cluster analysis using multivariate normal mixture models to detect differential gene expression with microarray data. Comput Stat Data Anal 51(2):641–658MATHCrossRef
Hruschka ER, Campello RJGB, Freitas AA, De Carvalho ACPLF (2009) A survey of evolutionary algorithms for clustering. IEEE Trans Syst Man Cybern Part C Appl Rev 39(2):133–155
Huang KY (2011) A hybrid particle swarm optimization approach for clustering and classification of datasets. Knowl Based Syst 24:420–426CrossRef
Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recogn Lett 31:651–666CrossRef
Jain AK, Dubes RC (1988) Algorithms clustering data. Prentice-Hall, Englewood cliffsMATH
Jensen F (1996) An introduction to bayesian networks. UCL Press/Springer, Berlin
Jiang H, Yi S, Li J, Yang F, Hu X (2010) Ant clustering algorithm with K-harmonic means clustering. Expert Syst Appl 37(12):8679–8684CrossRef
Jiang H, Li J, Yi S, Wang X, Hu X (2011) A new hybrid method based on partitioning-based DBSCAN and ant clustering. Expert Syst Appl 38(8):9373–9381CrossRef
Kao Y, Cheng K (2006) Ant colony optimization and swarm intelligence., An ACO-based clustering algorithm Springer, Berlin
Kao YT, Zahara E, Kao IW (2008) A hybridized approach to data clustering. Exp Syst Appl 34(3):1754–1762CrossRef
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Global Optim 39(3):459–471MATHMathSciNetCrossRef
Karaboga D, Ozturk C (2011) A novel clustering approach: artificial bee colony (ABC) algorithm. Appl Soft Comput 11:652–657CrossRef
Kaufman L, Rousseeuw P (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New York
Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mechanica 213(3–4):267–289MATHCrossRef
Kaveh A, Laknejadi A (2011) A novel hybrid charge system search and particle swarm optimization method for multi-objective optimization. Exp Syst Appl 38:15475–15488CrossRef
Kaveh A, Share AMAM, Moslehi M (2013) Magnetic charged system search: a new meta-heuristic algorithm for optimization. Acta Mechanica 224(1):85–107MATHCrossRef
Kaveh A, Mirzaeib B, Jafarvand A (2015) An improved magnetic charged system search for optimization of truss structures with continuous and discrete variables. Appl Soft Comput 28:400–410CrossRef
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceeding of IEEE international conference on neural networks (ICW), IV, pp 1942–1948
Krishna K, Murty MN (1999) Genetic k-means algorithm. IEEE Trans Syst Man Cybern Part B Cybern 29(3):433–439CrossRef
Kumar Y, Sahoo G (2014a) A charged system search approach for data clustering. Progress Artif Intell 2(2–3):53–166
Kumar Y, Sahoo G (2014b) A chaotic charged system search approach for data clustering. Informatica 38(3):149–61
Kumar Y, Sahoo G (2014c) A hybridize approach for data clustering based on cat swarm optimization. Int J Inf Commun Technol (in Press)
Kumar Y, Sahoo G (2015) An improved cat swarm optimization algorithm for clustering. Comput Intell Data Min 1:187–197
Kuo RJ, Lin LM (2010) Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering. Dec Support Syst 49:451–462
Lu Y, Lu S, Fotouhi F, Deng Y, Brown SJ (2004) FGKA: a fast genetic k-means clustering algorithm. In: Proceedings of the ACM symposium on applied computing, pp 622–623
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 1, pp 281–297
Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recognit 33(9):1455–1465CrossRef
Maulik U, Mukhopadhyay A (2010) Simulated annealing based automatic fuzzy clustering combined with ANN classification for analyzing microarray data. Comput Oper Res 37(8):1369–1380MATHCrossRef
McLachlan G, Krishnan T (1997) The EM algorithm and extensions. Wiley, New YorkMATH
Milan S, Hlavac V, Boyle R (1998) Image processing, analysis, and machine vision, 4th edn. Chapman and Hall, London
Mullen Robert J, Monekosso Dorothy, Barman Sarah, Remagnino Paolo (2009) A review of ant algorithms. Exp Syst Appl 36(6):9608–9617CrossRef
Murthy CA, Chowdhury N (1996) In search of optimal clusters using genetic algorithms. Pattern Recognit Lett 17(8):825–832
Niknam T, Amiri B (2010) An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Appl Soft Comput 10:183–197CrossRef
Price MN, Dehal PS, Arkin AP (2009) FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol 26(7):1641–1650
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput-Aided Des 43(3):303–315CrossRef
Sahoo AJ, Kumar Y (2014) Advances in signal processing and intelligent recognition systems., Modified teacher learning based optimization method for data clusteringSpringer, Berlin
Santosa B, Ningrum MK (2009) Cat swarm optimization for clustering. In: International conference on soft computing and pattern recognition (SOCPAR’09), pp 54–59
Satapathy SC, Naik A (2011) Data clustering based on teaching-learning-based optimization. In: Swarm, evolutionary, and memetic computing. Springer, Berlin, pp 148–56
Sarafrazi S, Nezamabadi-pour H, Saryazdi S (2011) Disruption: a new operator in gravitational search algorithm. Scien-tia Iranica D 18(3):539–548
Selim SZ, Alsultan K (1991) A simulated annealing algorithm for the clustering problem. Pattern Recognit 24(10):1003–1008MathSciNetCrossRef
Shelokar PS, Jayaraman VK, Kulkarni BD (2004) An ant colony approach for clustering. Analytica Chimica Acta 509(2):187–95CrossRef
Sinha AN, Das N, Sahoo G (2007) Ant colony based hybrid optimization for data clustering. Kybernetes 36(2):175–191
Sneath P (1957) The application of computers to taxonomy. J Gen Microbiol 17:201–226CrossRef
Sokal R, Michener C (1958) A statistical method for evaluating systematic relationships. Univ Kansas Sci Bull 38:1409–1438
Sorensen T (1948) A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyzes of the vegetation on Danish commons. Biologiske Skrifter 5:1–34
Sung CS, Jin HW (2000) A tabu-search-based heuristic for clustering. Pattern Recognit 33(5):849–858
Tsai CF, Tsai CW, Wu HC, Yang T (2004) ACODF: a novel data clustering approach for data mining in large databases. J Syst Softw 73(1):133–145
Teppola P, Mujunen SP, Minkkinen P (1999) Adaptive fuzzy C-means clustering in process monitoring. In: Chemometrics and intelligent laboratory systems 45(1):23–28
Trelea IC (2003) The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett 85:317–325MATHMathSciNetCrossRef
Tseng LY, Yang SB (1997) Genetic algorithms for clustering, feature selection and classification. IEEE Int Conf Neural Netw 3:1612–1616
Tseng LY, Yang SB (2001) A genetic approach to the automatic clustering problem. Pattern Recognit 34(2):415–424MATHCrossRef
Wang W, Yang J, Muntz R (1997) STING: a statistical information grid approach to spatial data mining. In: Proceedings of the 1997 international conference on very large data base (VLDB’97), Athens, Greek, pp 186–195
Webb GI (2000) Multiboosting: a technique for combining boosting and wagging. Mach Learn 40(2):159–196CrossRef
Xu R, Wunsch DC (2009) Clustering. Oxford, Wiley
Yan X, Zhu Y, Zou W, Wang L (2012) A new approach for data clustering using hybrid artificial bee colony algorithm. Neuro Comput 97:241–250
Yang Y, Kamel MS (2006) An aggregated clustering approach using multi-ant colonies algorithms. Pattern Recognit 39(7):1278–1289MATHCrossRef
Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. In: Proceedings of the ACM SIGMOD conference on management of data, pp 103–114
Zhou H, Yonghuai L (2008) Accurate integration of multi-view range images using k-means clustering. Pattern Recognit 41(1):152–175MATHCrossRef
Metadata
Title
Hybridization of magnetic charge system search and particle swarm optimization for efficient data clustering using neighborhood search strategy
Authors
Y. Kumar
G. Sahoo
Publication date
05-06-2015
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 12/2015
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-015-1719-0

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