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A survey: hybrid evolutionary algorithms for cluster analysis

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Abstract

Clustering is a popular data analysis and data mining technique. It is the unsupervised classification of patterns into groups. Many algorithms for large data sets have been proposed in the literature using different techniques. However, conventional algorithms have some shortcomings such as slowness of the convergence, sensitive to initial value and preset classed in large scale data set etc. and they still require much investigation to improve performance and efficiency. Over the last decade, clustering with ant-based and swarm-based algorithms are emerging as an alternative to more traditional clustering techniques. Many complex optimization problems still exist, and it is often very difficult to obtain the desired result with one of these algorithms alone. Thus, robust and flexible techniques of optimization are needed to generate good results for clustering data. Some algorithms that imitate certain natural principles, known as evolutionary algorithms have been used in a wide variety of real-world applications. Recently, much research has been proposed using hybrid evolutionary algorithms to solve the clustering problem. This paper provides a survey of hybrid evolutionary algorithms for cluster analysis.

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References

  • Abraham A, Das S, Konar A (2007) Kernel based automatic clustering using modified particle swarm optimization algorithm. In: Proceedings of the 9th annual conference on genetic and evolutionary computation 2007. GECCO’07

  • Abraham A, Das S, Konar A (2008) Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm. In: Pattern recognition letters, vol 29/2008. pp 688–699

  • Admane L, Benatchba K, Koudil M, Siad L, Maziz S (2006) AntPart: an algorithm for the unsupervised classification problem using ants. Appl Math Comput 180(1): 16–28

    Article  MathSciNet  MATH  Google Scholar 

  • Alam S, Dobbie G, Riddle P (2008) An evolutionary particle swarm optimization for data clustering. In: IEEE swarm intelligence symposium, 2008. SIS 2008, pp 1–6

  • Al-Sultan K (1995) A tabu search approach to the clustering problem. Pattern Recognit 28(9): 1443–1451

    Article  Google Scholar 

  • Aranha C, Iba H (2006) Using genetic algorithms to improve ant colony clustering. In: Proceedings of the 2006 Asia pacific workshop on genetic programming (ASPGP06) 2006

  • Azzag H, Monmarche N, Slimane M, Venturini G, Guinot C (2003) AntTree: a new model for clustering with artificial ants. In: Proceedings of the 2003 congress on evolutionary computation, Canberra, CEC’03, vol 4/2003. IEEE press, pp 2642–2647

  • Azzag H, Guinot C, Venturini G (2006) Data and text mining with hierarchical clustering ants. In: Abraham A, Grosan C, Ramos V (eds) Swarm intelligence in data mining. pp 153–190

  • Bäck T, Fogel DB, Michalewicz Z (eds) (2000a) Evolutionary computation 1: basic algorithms and operators. Institute of physics publishing, Bristol, UK

  • Bäck T, Fogel DB, Michalewicz Z (eds) (2000b) Evolutionary computation 2: basic algorithms and operators. Institute of physics publishing, Bristol, UK

  • Berkhin P (2002) Survey clustering data mining techniques. Technical report, Accrue software, San Jose

  • Bin W, Zhongzhi S (2001) A clustering algorithm based on swarm intelligence. In: Proceedings of the international conference on Info-tech and Info-net. Beijing, pp 58–66

  • Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford university press, Inc, New York

    MATH  Google Scholar 

  • Boryczka U (2008) Ant clustering algorithm. In: conference intelligent information systems 2008. pp 377–386

  • Brown D, Huntley C (1992) A practical application of simulated annealing to clustering. Pattern Recognit 25(4): 401–412

    Article  Google Scholar 

  • Cano JR, Cordón O, Herrera F, Sánchez L (2002) A GRASP algorithm for clustering. In: Garijo FJ, Riquelme JC, Toro M (eds) IBERAMIA 2002, LNAI, vol 2527/2002. Springer, Berlin, pp 214–223

  • Chen Y-F, Fattah CA, Liu Y-S, Yan G (2004) HDACC: a heuristic density-based ant colony clustering algorithm. In: Proceedings of the intelligent agent technology. IEEE/WIC/ACM international conference, 2004. pp 397–400

  • Chi S, Yang CC (2006) Integration of ant colony SOM and k–means for clustering analysis. In: Knowledge based intelligent information and engineering systems. LNCS, vol 4251/2006. Springer, pp 1–8

  • Chi S, Yang CC (2008) A two-stage clustering method combining ant colony SOM and K-means. J Inf Sci Eng Inst Inf Sci 24: 1445–1460

    Google Scholar 

  • Chiu C-Y, Lin C-H (2007) Cluster analysis based on artificial immune system and ant algorithm. In: Proceedings of the third international conference on natural computation, IEEE computer society, vol 3/2007. pp 647–650

  • Cohen SCM, de Castro LN (2006) Data clustering with particle swarms. In: IEEE congress on evolutionary computation 2006. CEC 2006, pp 1792–1798

  • Cordon O (2005) Hybridizing evolutionary computation and ant colony optimization: application to fuzzy rule learning and bioinformatics problems. TIC2003-00877

  • Cowgill M, Harvey R, Watson L (1999) A genetic algorithm approach to cluster analysis. Comput Math Appl 37: 99–108

    Article  MathSciNet  MATH  Google Scholar 

  • Cui X, Potok TE, Palathingal P (2005) Document clustering using particle swarm optimization. In: Proceedings 2005 IEEE swarm intelligence symposium 2005. SIS 2005, pp 185–191

  • Cui X, Potok TE (2006) Document clustering analysis based on hybrid PSO + K-means algorithm. J Comput Sci 2006. ISSN 1549–3636:27–33

    Google Scholar 

  • De Castro LD, Timmis J (2002) Artificial immune systems: a new computational intelligence approach. Springer, Heidelberg

    MATH  Google Scholar 

  • Deneubourg J-L, Gross S, Franks NR, Sendova-Franks A, Detrain C, Chretien L (1991) The dynamics of collective sorting: robot-like ants and ant-like robots. In: Proceedings of the first international conference on simulation of adaptive behavior: from animals to animats 1. MIT press, Cambridge. pp 356–363

  • Domínguez E, Muñoz J (2007) A hybrid algorithm for solving clustering problems. In: Corchado E et al (eds) Innovations in hybrid intelligent systems, ASC 44. Springer, Berlin, pp 128–135

  • Dong J, Qi M (2009a) A new clustering algorithm based on PSO with the jumping mechanism. In: 2009 IEEE third international symposium on intelligent information technology application

  • Dong J, Qi M (2009b) A new algorithm for clustering based on particle swarm optimization and K-means. In: International conference on artificial intelligence and computational intelligence 2009. AICI’09, pp 264–268

  • Dorigo M, Di Caro G (1999) Ant colony optimization: a new metaheuristic. In: Corne D, Dorigo M, Glover F (eds) New ideas in optimization. McGraw, London, pp 11–32

    Google Scholar 

  • Dorigo M, Stutzle T (2002) The ant colony optimization metaheuristic: algorithms, applications, and advances. In: Glover F, Kochenberger G (eds) Handbook of metaheuristics. Kluwer academic publishers, Dordrecht, pp 251–285

    Google Scholar 

  • Dorigo M, Stutzle T (2004) Ant colony optimization. The MIT press, Cambridge

    Book  MATH  Google Scholar 

  • Dorigo M, Maniezzo V, Colorni A (1991) Positive feedback as a search strategy. Technical report No. 91–016, Politecnico di Milano, Italy

  • Dorigo M, Di Caro G, Gambarella LM (1999) Ant algorithms for discrete optimization. Artif Life 5(2): 137–172

    Article  Google Scholar 

  • Duran B, Odell P (1974) Cluster analysis: a survey. Springer, Berlin

    MATH  Google Scholar 

  • El-Feghi I, Errateeb M, Ahmadi M, Sid-Ahmed MA (2009) An adaptive ant-based clustering algorithm with improved environment perception. In: Proceedings of the 2009 IEEE international conference on systems, man, and cybernetics. San Antonio

  • Esmin AAA, Pereira DL, de Araujo F (2008) Study of different approach to clustering data by using particle swarm optimization algorithm. In: IEEE congress on evolutionary computation 2008. CEC 2008, pp 1817–1822

  • Ester M, Kriegel H-P, Sander J, Xu X (1996) A density based algorithm for discovering clusters in large spatial databases with noise. In: Simuoudis E, Han J, Fayyard U (eds) Second international conference on knowledge discovery and data mining. AAAI press, Portland, pp 226–231

  • Fathian M, Amiri B, Maroosi A (2007) Application of honey bee mating optimization algorithm on clustering. Appl Math Comput 190(2): 1502–1513

    Article  MathSciNet  MATH  Google Scholar 

  • Feo TA, Resende MGC (1995) Greedy randomized adaptive search procedure. J Glob Optim 6: 109–133

    Article  MathSciNet  MATH  Google Scholar 

  • Fernandes C, Mora AM, Merelo JJ, Ramos V, Laredo JLJ (2008) KohonAnts: a self-organizing ant algorithm for clustering and pattern classification. http://arxiv.org/abs/0803.2695v1

  • Franks NR, Sendova-Franks AB (1992) Brood sorting by ants: distributing the workload over the work surface. Behav Ecol Sociobiol 30: 109–123

    Article  Google Scholar 

  • Freitas AA (2001) A survey of evolutionary algorithms for data mining and knowledge discovery. In: Advances in evolutionary computing: theory and applications, Natural computing series. Springer

  • Fu H (2008) A novel clustering algorithm with ant colony optimization. In: 2008 IEEE pacific-asia workshop on computational intelligence and industrial applications, PACIIA 2008

  • Fun Y, Chen CY (2004) Particle swarm optimization algorithm and its application to clustering analysis. In: 2004 IEEE international conference on networking, sensing and control. pp 789–794

  • Fun Y, Chen CY (2005) Alternative KPSO-clustering algorithm. J Sci Eng 8: 165–174

    Google Scholar 

  • Ganti V, Gehrke J, Ramakrishnan R (1999) CACTUS—clustering categorical data using summaries. In: International conference on knowledge discovery and data mining. San Diego, pp 73–83

  • Ghosh A, Halder A, Kothari M, Ghosh S (2008) Aggregation pheromone density based data clustering. Inf Sci 178(13): 2816–2831

    Article  Google Scholar 

  • Glover, F, Laguna, M (eds) (1997) Tabu search. Kluwer Academic Publishers, Norwell

    MATH  Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison, Reading

    MATH  Google Scholar 

  • Gu Y, Hall LO, Goldgof DB (2009) Ant clustering using ensembles of partitions. In: Proceedings of the 8th international workshop on multiple classifier systems, LNCS, vol 5519/2009. Springer, Berlin, pp 283–292

  • Guha S, Rastogi R, Shim K (1998) CURE: an efficient clustering algorithm for large databases. In: ACM SIGMOD international conference on the management of data. Seatle, pp 73–84

  • Güngör Z, A (2007) K-harmonic means data clustering with simulated annealing heuristic. Appl Math Comput 184(2): 199–209

    Article  MathSciNet  MATH  Google Scholar 

  • Hamdi A, Monmarché N, Alimi AM, Slimane M (2008) SwarmClass: a novel data clustering approach by a hybridization of an ant colony with flying insects. In: ANTS 2008. LNCS, vol 5217/2008. Springer, Berlin, pp 411–412

  • Han J, Kamber M (2001) Data mining: concepts and techniques. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Handl J, Knowles J, Dorigo M (2003a) On the performance of ant-based clustering. Frontiers in artificial intelligence and applications, vol 104. pp 204–213

  • Handl J, Knowles J, Dorigo M (2003b) Ant-based clustering: a comparative study of its relative performance with respect to k-means, average link and 1d-som. IRIDIA-Technical report series, xxx

  • Handl J, Knowles J, Dorigo M (2006) Ant-based clustering and topographic mapping. Artif Life 12(1): 35–61

    Article  Google Scholar 

  • He Y, Hui SC (2009) Exploring ant-based algorithms for gene expression data analysis. Artifl Intell Med. In press, corrected proof, available online 18 April 2009

  • Herrmann L, Ultsch A (2009) Clustering with swarm algorithms compared to emergent SOM. In: Advances in self-organizing maps. LNCS, vol 5629/2009. Springer, Berlin, pp 80–88

  • Ho CK, Ewe HT (2005) A hybrid ant colony optimization approach (hACO) for constructing load-balanced clusters. In: 2005 IEEE congress on evolutionary computation, vol 3/2005, pp 2010–2017

  • Huang X, Yang Y, Niu X (2007) Towards improving ant-based clustering—an chaotic ant clustering algorithm. In: Proceedings of the 2007 international conference on computation intelligence and security workshops, 2007. pp 421–424

  • Huang Y-S, Deng J-J (2008) Short-term load forecasting based on ant colony fuzzy clustering and SVM algorithm. In: Proceedings of the 2008 fourth international conference on natural computation, 2008, vol 02. pp 162–166

  • Ingaramo DA, Leguizamon G, Errecalde M (2005) Adaptive clustering with artificial ants. J Comput Sci Technol 5(4):264–271

    Google Scholar 

  • Izakian H, Abraham A, Snášel V (2009) Fuzzy clustering using hybrid fuzzy c-means and fuzzy particle swarm optimization. In: IEEE world congress on nature & biologically inspired computing 2009. (NaBIC 2009), pp 1690–1694

  • Jain A, Dubes R (1998) Algorithms for clustering data. Prentice Hall, New Jersey

    Google Scholar 

  • Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31: 264–323

    Article  Google Scholar 

  • Janson S, Merkle D (2005) A new multi-objective particle swarm optimization algorithm using clustering applied to automated docking. In: MJ Blesa et al (eds) LNCS, vol 3636/2005. HM 2005, Springer, pp 128–141

  • Kanade PM, Hall LO (2003) Fuzzy ants as a clustering concepts. In: Proceedings of the 22nd international conference of the north american fuzzy information processing society (NAFIPS 2003). pp 227–232

  • Kao Y, Cheng K (2006) An ACO-based clustering algorithm. In: Dorigo et al (eds) ANTS 2006. LNCS, vol 4150/2006. Springer, Berlin, pp 340–347

  • Kao Y, Lee S-Y (2009) Combining K-means and particle swarm optimization for dynamic data clustering problems. In: IEEE international conference on intelligent computing and intelligent systems, 2009. ICIS 2009. pp 757–761

  • Kao IW, Tsai CY, Wang YC (2007a) An effective particle swarm optimization method for data clustering. In: IEEE international conference on industrial engineering and engineering management 2007. IEEM 2007, pp 548–552

  • Kao Y-T, Zahara E, Kao I-W (2007b) A hybridized approach to data clustering. Expert Syst Appl. doi:10.1016/j.eswa.2007.01.028

  • Karypis G, Han E-H, Kumar V (1999) CHAMELEON: a hierarchical clustering algorithm using dynamic modeling. Computer 32: 32–68

    Article  Google Scholar 

  • Kaufman L, Russeeuw P (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New York

    Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international joint conference on neural networks. IEEE press, IJCNN 95, Piscataway, pp 1942–1948

  • Kennedy J, Eberhart RC, Shi Y (2001) Swarm intelligence. The Morgan Kaufmann series in artificial intelligence. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220: 671–680

    Article  MathSciNet  Google Scholar 

  • Korürek M, Nizam A (2008) A new arrhythmia clustering technique based on ant colony optimization. J Biomed Inf 41(6): 874–881

    Article  Google Scholar 

  • Kuo RJ, Kuo YP, Chen K-Y (2005) Developing a diagnostic system through integration to fuzzy case-based reasoning and fuzzy ant colony system. Expert Syst Appl 28: 783–797

    Article  Google Scholar 

  • Kuo RJ, Cha CL, Chou SH (2006) Developing a diagnostic system through integration of ant colony optimization systems and case-based reasoning. Int J Adv Manuf Technol. Springer-Verlag 30: 750–760

    Article  Google Scholar 

  • Kuo RJ, Wang MJ, Huang TW (2009) An application of particle swarm optimization algorithm to clustering analysis. Soft computing—a fusion of foundations, methodologies and applications. Springer, Berlin

    Google Scholar 

  • Labroche N, Monmarche N, Venturini G (2003) AntClust: ant clustering and web usage mining. In: Proceedings of genetic and evolutionary computation conference (GECCO-2003). LNCS, vol 2723. Springer, Chicago, pp 25–36

  • Li Z, Tan T-Z (2006) A combinational clustering method on artificial immune system and support vector machine. In: Gabrys B, Howlett RJ, Jain LC (eds) KES 2006. LNCS, vol 4251/2006. Springer, Heidelberg, pp 153–162

  • Li J, Fan H, Da Y, Zhang C (2008) Kernel function clustering based on ant colony algorithm. In: Proceedings of the 2008 fourth international conference on natural computation, ICNC 2008, vol 07. pp 645–649

  • Liu B, Pan J, (Bob) McKay RI (2009) Entropy-based metrics in swarm clustering. Int J Intell Syst 24: 989–1011

    Article  MATH  Google Scholar 

  • Ltu S, Dou Z-T, Li F, Wang Y-L (2004) A new ant colony clustering algorithm based on DBSCAN. In: Proceedings of the third international conference on machine learning and cybernetics. Sbangbai

  • Lumer E, Faieta B (1994) Diversity and adaptation in populations of clustering ants. In: Cliff D, Husbands P, Meyer J-A, Wilson SW (eds) From animals to animats 3: proceedings of the third international conference on simulation of adaptive behavior. MIT press/Bradford books, Cambridge, pp 501–508

  • MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: 5th Berkeley symposium on mathematics, statistics and probability. pp 281–296

  • Marinakis Y, Marinaki M, Matsatsinis N (2007) A hybrid particle swarm optimization algorithm for cluster analysis. In: Song I-Y, Eder J, Nguyen TM (eds) DaWaK 2007. LNCS, vol 4654/2007. Springer, Berlin, pp 241–250

  • Marinakis Y, Marinaki M, Matsatsinis N (2008) A hybrid clustering algorithm based on multi-swarm constriction PSO and GRASP. In: Song I-Y, Eder J, Nguyen TM (eds) DaWaK 2008. LNCS, vol 5182/2008. Springer, Berlin, pp 186–195

  • Marinakis Y, Marinaki M, Matsatsinis N (2009) A hybrid bumble bees mating optimization—GRASP algorithm for clustering. In: Corchado et al (eds) HAIS 2009. LNCS, vol 5572/2009. Springer, Berlin, pp 549–556

  • Maroosi A, Amiri B (2010) A new clustering algorithm based on hybrid global optimization based on a dynamical systems approach algorithm. Expert Syst Appl 37: 5645–5652

    Article  Google Scholar 

  • Meng Y, Li X (2007) Application of k-means algorithm based on ant clustering algorithm in macroscopic planning of highway transportation hub. In: First IEEE international symposium on information technologies and applications in education, 2007. ISITAE’07, pp 483–488

  • Monmarche N, Slimane N, Venturini G (1999a) Ant-class: discovery of clusters in numeric data by an hybridization of an ant colony with k-means algorithm. Rapport interne 213, Laboratoire d’ informa-tique de l’ universite de tours, E3i tours

  • Monmarche N, Slimane N, Venturini G (1999b) On improving clustering in numerical databases with artificial ants. In: LNCS, vol 1674/1999. Springer, Berlin, pp 626–635

  • Nasraoui O, Gonzalez F, Cardona C, Rojas C, Dasgupta D (2003) A scalable artificial immune system model for dynamic unsupervised learning. In: Cantú-Paz E, Foster JA, Deb K, Davis L, Roy R, O’Reilly U-M, Beyer H-G, Kendall G, Wilson SW, Harman M, Wegener J, Dasgupta D, Potter MA, Schultz A, Dowsland KA, Jonoska N, Miller J, Standish RK (eds) GECCO 2003. LNCS, vol 2723/2003. Springer, Heidelberg, pp 219–230

  • Ng R, Han J (2002) CLARANS: a method for clustering objects for spatial data mining. IEEE Trans Knowl Data Eng 14(5): 1003–1016

    Article  Google Scholar 

  • Ngenkaew W, Ono S, Nakayama S (2008) Pheromone-based concept in ant clustering. In: 2008 Proceedings of 3rd international conference on intelligent system and knowledge engineering

  • Niknam T, Nayeripour M, Firouzi BB (2008a) Application of a new hybrid optimization algorithm for cluster analysis. In: Proceedings of world academy of science, engineering and technology, vol 36

  • Niknam T, Nayeripour M, Firouzi BB (2008b) An efficient hybrid evolutionary algorithm for cluster analysis. World Appl Sci J 4(2): 300–307

    Google Scholar 

  • Niknam T, Nayeripour M, Firouzi BB (2008c) A new evolutionary algorithm for cluster analysis. In: Proceedings of world academy of science, engineering and technology, vol 36. ISSN 2070–3740

  • Niknam T, Amiri B, Olamaei J, Arefi A (2009) An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering. J Zhejiang Univ Sci A 10(4): 512–519

    Article  MATH  Google Scholar 

  • Omran M, Salman A, Engelbrecht A (2002) Image classification using particle swarm optimization. In: Wang L, Tan KC, Furukhashi T, Kim J-H, Yao X (eds) Proceedings of the fourth Asia-pacific conference on simulated evolution and learning, SEAL’02. IEEE press, Piscataway, pp 370–374

  • Omran M, Salman A, Engelbrecht A (2005) Dynamic clustering using particle swarm optimization with applications in unsupervised image classification. In: Proceedings of world academy of science, engineering and technology, vol 9/2005. ISSN 1307–6884

  • Oprisan SA, Holban V, Moldoveanu B (1996) Functional self-organization performing wide-sense stochastic processes. Phys Lett A 216: 303–306

    Article  Google Scholar 

  • Paterlini S, Krink T (2006) Differential evolution and particle swarm optimization in partitional clustering. Comput Stat Data Anal 50(5): 1220–1247

    Article  MathSciNet  Google Scholar 

  • Pellegrini P, Moretti E (2009) A computational analysis on a hybrid approach quick-and-dirty ant colony optimization. Appl Math Sci 3(24): 1127–1140

    MathSciNet  Google Scholar 

  • Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization—an overview. Swarm Intell 1(1): 33–57

    Article  Google Scholar 

  • Qu J, Liu X (2007) A quick ant clustering algorithm. In: Proceedings of the fourth international conference on fuzzy systems and knowledge discovery, vol 01/2007. pp 722–725

  • Qu J, Liu X (2009) A revised ant clustering algorithm with obstacle constraints. In: Proceedings of the 2009 WRI world congress on computer science and information engineering, vol 03/2009. pp 679–683

  • Quadfel S, Batouche M (2007) An efficient ant algorithm for swarm-based image clustering. J Comput Sci 3(3): 162–167

    Article  Google Scholar 

  • Ramos V, Merelo JJ (2002) Self-organized stigmergic document maps: environment as a mechanism for context learning. In: first spanish conference on evolutionary and bio-inspired algorithms. Spain, pp 284–293

  • Ramos GN, Hatakeyama Y, Dong F, Hirota K (2009) Hyperbox clustering with ant colony optimization and its application to medical risk profile recognition. Appl Soft Comput 9(2): 632–640

    Article  Google Scholar 

  • Runkler TA, Katz C (2006) Fuzzy clustering by particle swarm optimization. In: IEEE international conference on fuzzy systems, Canada, 2006. pp 601–608

  • Saatchi S, Hung CC (2005) Hybridization of the ant colony optimization with the k-means algorithm for clustering. In: SCIA 2005, LNCS, vol 3540/2005. Springer, Berlin, pp 511–520

  • Sadeghi Z, Teshnehlab M (2008) Ant colony clustering by expert ants. In: 11th international conference on computer and information technology, 2008. ICCIT 2008, pp 94–100

  • Shang G, Zaiyue Z, Xiaoru Z, Cungen C (2008) A new hybrid ant colony algorithm for clustering problem. In: Proceedings of the 2008 international workshop on education technology and training & 2008 international workshop on geoscience and remote sensing, vol 1. pp 645–648

  • Sharma A, Omlin CW (2009) Performance comparison of particle swarm optimization with traditional clustering algorithms used in self-organization map. Int J Inf Math Sci (World academy of science, engineering and technology) 5(1): 1–12

    Google Scholar 

  • Shelokar PS, Jayaraman VK, Kulkarni BD (2004) An ant colony approach for clustering. Anal Chim Acta 509: 187–195

    Article  Google Scholar 

  • Shen H-Y, Peng X-Q, Wang J-N, Hu Z-K (2005) A mountain clustering based on improved PSO algorithm. In: Wang L, Chen K, Ong YS (eds) ICNC 2005, LNCS, vol 3612/2005. Springer, Berlin, pp 477–481

  • Sherafat V, Nunes de Castro L, Hruschka ER (2004) TermitAnt: an ant clustering algorithm improved by ideas from termite colonies. In: Pal NR et al (eds) LNCS, vol 3316/2004. ICONIP 2004. Springer, Berlin, pp 1088–1093

  • Siraj MM, Maarof MA, Hashim SM (2009) A hybrid approach for automated alert clustering and filtering in intrusion alert analysis. IJCTE 1(87): 536

    Google Scholar 

  • Srinov S, Kurutach W (2006) Combination artificial ant clustering and k-pso clustering approach to network security model. In: International conference on hybrid information technology, 2006. (ICHIT’06), vol 2. pp 128–134

  • Sun J, Xu W, Ye B (2006) Quantum-behaved particle swarm optimization clustering algorithm. In: Li X, Zaiane OR, Li Z (eds) LNAI, vol 4093/2006. ADMA 2006. Springer, Berlin, pp 340–347

  • Tao Z, Xiaodong LV, Zaixu Z (2007) An improved clustering algorithm based on ant colony approach. In: Proceedings of the 2007 international conference on computational intelligence and security workshops. pp 437–440

  • 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

    Article  Google Scholar 

  • Van der Merwe D, Engelbrecht A (2003) Data clustering using particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation, 2003. CEC 2003, Canbella, pp 215–220

  • Vizine A, de Castro LN, Hruschka ER, Gudwin RR (2005) Towards improving clustering ants: an adaptive clustering algorithm. Inf J 29: 143–153

    MATH  Google Scholar 

  • Wang L, Liu Y, Zhao X, Xu Y (2006) Particle swarm optimization for c-means clustering. In: The sixth world congress on intelligent control and automation 2006. WCICA 2006, vol 2/2006. pp 6055–6058

  • Wang Y, Li R-W, Li B, Zhang P-J, Li Y-H (2007) Research on an ant colony ISODATA algorithm for cluster analysis in real time computer simulation. In: Proceedings of the second workshop on digital media and its application in museum and heritage, 2007. pp 223–229

  • Wang J, Liang J, Che J, Sun D (2008) A hybrid evolutionary algorithm based on ACO and PSO for real estate early warning system. In: International conference on computer science and information technology, 2008. (ICCSIT 2008), pp 167–171

  • Wang F, Zhang D, Bao N (2009a) Fuzzy document clustering based on ant colony algorithm. In: Proceedings of the 6th international symposium on neural networks: advances in nueral networks—part II. LNCS, vol 5552/2009. pp 709–716

  • Wang X, Shen J, Tang H (2009b) Novel hybrid document clustering algorithm based on ant colony and agglomerate. In: Proceedings of the 2009 second international symposium on knowledge acquisition and modeling, vol 03. IEEE Computer Society, Washington, pp 65–68

  • Weili Z (2009) An improved entropy-based ant clustering algorithm. In: Proceedings of the 2009 WASE international conference on information engineering, vol 2/2009. pp 41–44

  • Xiao L, Zhang O (2009) A hybrid ant colony algorithm for the grain distribution centers location. ICIC 2009. LNCS, vol 5754/2009. Springer, Berlin, pp 112–119

  • Xu R, Wunsch D II (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3): 645–678

    Article  Google Scholar 

  • Xu X, Chen L, Chen Y (2004) A 4 C: an adaptive artificial ants clustering algorithm. In: Proceedings of the 2004 IEEE symposium on computational intelligence in bioinformatics and computational biology, 2004. CIBCB’04. pp 268–275

  • Yang Y, Kamel MS (2006) An aggregated clustering approach using multi-ant colonies algorithms. Pattern Recognit 39(7): 1278–1289

    Article  MATH  Google Scholar 

  • Yang Y, Kamel M, Jin F (2005) Topic discovery from document using ant-based clustering combination. In: APWeb 2005. LNCS, vol 3399/2005. Springer, Berlin, pp 100–108

  • Yang B, Chen Y, Zhao Z, Wuhan CL (2007) A hybrid evolutionary algorithm by combination of PSO and GA for unconstrained and constrained optimization problems. In: IEEE international conference on control and automation, 2007. ICCA 2007, pp 166–170

  • Yang C-S, Chuang L-Y, Ke C-H, Yang C-H (2008) Comparative particle swarm optimization (CPSO) for solving optimization problems. In: IEEE conference on research, innovation and vision for the future, 2008. pp 86–90

  • Yang F, Sun T, Zhang C (2009) An efficient hybrid data clustering method based on K-harmonic means and particle swarm optimization. Expert Syst Appl 36(6): 9847–9852

    Article  Google Scholar 

  • Yuqing P, Xiangdan H, Shang L (2003) The K-means clustering algorithm based on density and ant colony. In: IEEE international conference on neural networks and signal processing, Nanjing, 2003

  • Zait M, Messatfa H (1997) A comparative study of clustering methods. FGCS J, Special issue data min (1997)

  • Zhang L, Caol Q, Lee J (2005) A modified clustering algorithm based on swarm intelligence. In: Wang L, Chen K, Ong YS (eds) ICNC 2005. LNCS, vol 3612/2005. Springer, Berlin, pp 535–542

  • Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. In: Proceedings ACM SIGMOD conference management of data, 1996. pp 103–114

  • Zhang X, Zhang Q, Fan Z, Deng G, Zhang C (2008a) Clustering spatial data with obstacles using improved ant colony optimization and hybrid particle swarm optimization. In: Proceedings of the 2008 fifth international conference on fuzzy systems and knowledge discovery, vol 02. pp 424–428

  • Zhang F, Ma Y, Hou N, Liu H (2008b) An ant-based fast text clustering approach using pheromone. In: Proceedings of the 2008 fifth international conference on fuzzy systems and knowledge discovery, vol 02. pp 385–389

  • Zhao F, Hong Y, Yu D, Yang Y, Zhang Q, Yi H (2007) A hybrid algorithm based on particle swarm optimization and simulated annealing to holon task allocation for holonic manufacturing system. Int J Adv Manuf Technol Springer 32: 1021–1032

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  1. Department of Computer Science, A.V.V.M. Sri Pushpam College (Autonomous), Poondi, Tamil Nadu, India

    Mohamed Jafar Abul Hasan & Sivakumar Ramakrishnan

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  1. Mohamed Jafar Abul Hasan
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  2. Sivakumar Ramakrishnan
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Correspondence to Mohamed Jafar Abul Hasan.

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Abul Hasan, M.J., Ramakrishnan, S. A survey: hybrid evolutionary algorithms for cluster analysis. Artif Intell Rev 36, 179–204 (2011). https://doi.org/10.1007/s10462-011-9210-5

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