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Soft Computing

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05.06.2015 | Focus

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

verfasst von: Y. Kumar, G. Sahoo

Erschienen in: Soft Computing | Ausgabe 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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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

Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140MATHMathSciNet

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

Hartigan JA (1975) Clustering algorithms. Wiley, New YorkMATH

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

Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184MathSciNetCrossRef

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

Kohonen T (1990) The self-organizing maps. Proc IEEE 78(9):1464–1480CrossRef

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

Kuo RJ, Wang HS, Hu TL, Chou SH (2005) Application of ant K-means on clustering analysis. Comput Math Appl 50(10):1709–1724MATHMathSciNetCrossRef

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 A (2002) Statistical pattern recognition. Wiley, New JerseyMATHCrossRef

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

Zadeh L (1965) Fuzzy sets. Inf Control 8:338–353MATHMathSciNetCrossRef

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

Titel: Hybridization of magnetic charge system search and particle swarm optimization for efficient data clustering using neighborhood search strategy
verfasst von: Y. Kumar
G. Sahoo
Publikationsdatum: 05.06.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 12/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-015-1719-0

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