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Dynamic clustering using combinatorial particle swarm optimization

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Abstract

Combinatorial Particle Swarm Optimization (CPSO) is a relatively recent technique for solving combinatorial optimization problems. CPSO has been used in different applications, e.g., partitional clustering and project scheduling problems, and it has shown a very good performance. In partitional clustering problem, CPSO needs to determine the number of clusters in advance. However, in many clustering problems, the correct number of clusters is unknown, and it is usually impossible to estimate. In this paper, an improved version, called CPSOII, is proposed as a dynamic clustering algorithm, which automatically finds the best number of clusters and simultaneously categorizes data objects. CPSOII uses a renumbering procedure as a preprocessing step and several extended PSO operators to increase population diversity and remove redundant particles. Using the renumbering procedure increases the diversity of population, speed of convergence and quality of solutions. For performance evaluation, we have examined CPSOII using both artificial and real data. Experimental results show that CPSOII is very effective, robust and can solve clustering problems successfully with both known and unknown number of clusters. Comparing the obtained results from CPSOII with CPSO and other clustering techniques such as KCPSO, CGA and K-means reveals that CPSOII yields promising results. For example, it improves 9.26 % of the value of DBI criterion for Hepato data set.

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References

  1. Pedrycz W (2005) Knowledge-based clustering. Wiley, New York. doi:10.1002/0471708607.fmatter

    Book  MATH  Google Scholar 

  2. Frigui H, Krishnapuram R (1999) A robust competitive clustering algorithm with applications in computer vision. IEEE Trans Pattern Anal Mach Intell 21(5):450–465

    Article  Google Scholar 

  3. Xu R, Wunsch DC (2010) Clustering algorithms in biomedical research: a review. IEEE Rev Biomed Eng 3(1):120–154

    Google Scholar 

  4. 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 10(4):512–519. doi:10.1631/jzus.A0820196

    Article  MATH  Google Scholar 

  5. Papadimitriou CH, Steiglitz K (1998) Combinatorial optimization: algorithms and complexity. Dover, New York

    MATH  Google Scholar 

  6. Clerc M (2006) Particle swarm optimization. Wiley-ISTE, New York

    Book  MATH  Google Scholar 

  7. Jarboui B, Cheikh M, Siarry P, Rebai A (2007) Combinatorial particle swarm optimization (CPSO) for partitional clustering problem. Appl Math Comput 192(2):337–345. doi:10.1016/j.amc.2007.03.010

    Article  MathSciNet  MATH  Google Scholar 

  8. Yucheng K, Szu-Yuan, L (2009) Combining K-means and particle swarm optimization for dynamic data clustering problems. In: IEEE international conference on intelligent computing and intelligent systems (ICIS), 20–22 Nov. 2009, pp 757–761

    Google Scholar 

  9. Hruschka ER, Campello RJGB, Freitas AA, Carvalho ACPLF (2009) A survey of evolutionary algorithms for clustering. IEEE Trans Syst Man Cybern, Part C, Appl Rev 39(2):133–155

    Article  Google Scholar 

  10. Omran M, Salman A, Engelbrecht (2005) A dynamic clustering using particle swarm optimization with application in unsupervised image classification. In: 5th world enformatika conference (ICCI 2005), Prague, Czech Republic, Citeseer, pp 199–204

  11. Shin K, Jeong Y-S, Jeong M (2012) A two-leveled symbiotic evolutionary algorithm for clustering problems. Appl Intell 36(4):788–799. doi:10.1007/s10489-011-0295-y

    Article  Google Scholar 

  12. Hruschka ER, Campello RJGB, de Castro LN (2006) Evolving clusters in gene-expression data. Inf Sci 176(13):1898–1927. doi:10.1016/j.ins.2005.07.015

    Article  Google Scholar 

  13. Hruschka ER, Ebecken NF (2003) A genetic algorithm for cluster analysis. Intell Data Anal 7(1):15–25

    Google Scholar 

  14. Ma PCH, Chan KCC, Yao X, Chiu DKY (2006) An evolutionary clustering algorithm for gene expression microarray data analysis. IEEE Trans Evol Comput 10(3):296–314

    Article  Google Scholar 

  15. Özyer T, Zhang M, Alhajj R (2011) Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation. Appl Intell 35(1):110–122. doi:10.1007/s10489-009-0206-7

    Article  Google Scholar 

  16. Özyer T, Alhajj R (2009) Parallel clustering of high dimensional data by integrating multi-objective genetic algorithm with divide and conquer. Appl Intell 31(3):318–331. doi:10.1007/s10489-008-0129-8

    Article  Google Scholar 

  17. http://www.isical.ac.in/~sushmita

  18. Karthi R, Arumugam S, Rameshkumar K (2008) Comparative evaluation of particle swarm optimization algorithms for data clustering using real world data sets. Int J Comput Sci Netw Secur 8(1):203–212

    Google Scholar 

  19. Bandyopadhyay S, Maulik U (2002) Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognit 35(6):1197–1208. doi:10.1016/s0031-3203(01)00108-x

    Article  MATH  Google Scholar 

  20. Liu Y, Wu X, Shen Y (2011) Automatic clustering using genetic algorithms. Appl Math Comput 218(4):1267–1279. doi:10.1016/j.amc.2011.06.007

    Article  MathSciNet  MATH  Google Scholar 

  21. Karthi R, Arumugam S, Kumar K (2009) Discrete particle swarm optimization algorithm for data clustering. In: Nature inspired cooperative strategies for optimization (NICSO), pp 75–88

    Chapter  Google Scholar 

  22. Latiff NM A, Tsimenidis CC, Sharif BS, Ladha C (2008) Dynamic clustering using binary multi-objective particle swarm optimization for wireless sensor networks. In: IEEE 19th international symposium on personal, indoor and mobile radio communications (PIMRC), 15–18 Sept. 2008, pp 1–5

    Google Scholar 

  23. Paoli A, Melgani F, Pasolli E (2009) Clustering of hyperspectral images based on multiobjective particle swarm optimization. IEEE Trans Geosci Remote Sens 47(12):4175–4188

    Article  Google Scholar 

  24. Niknam T, Amiri B (2010) An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Appl Soft Comput 10(1):183–197. doi:10.1016/j.asoc.2009.07.001

    Article  Google Scholar 

  25. Supratid S, Kim H (2009) Modified fuzzy ants clustering approach. Appl Intell 31(2):122–134. doi:10.1007/s10489-008-0117-z

    Article  Google Scholar 

  26. Falkenauer E (1998) Genetic algorithms and grouping problems. Wiley, New York

    Google Scholar 

  27. Kennedy J, Eberhart, R (1995) Particle swarm optimization. In: IEEE international conference on neural networks. Nov/Dec, 1995, pp 1942–1948

    Google Scholar 

  28. Kao YT, Zahara E, Kao IW (2008) A hybridized approach to data clustering. Expert Syst Appl 34(3):1754–1762. doi:10.1016/j.eswa.2007.01.028

    Article  Google Scholar 

  29. Premalatha K, Natarajan A (2009) A new approach for data clustering based on PSO with local search. Comput Inf Sci 1(4):139–145

    MathSciNet  Google Scholar 

  30. Yang S, Li Y, Hu X, Pan R (2006) Optimization study on k-value of K-means algorithm. Syst Eng-Theory Pract, Inst China Syst Eng, Beijing 26(2):97–101

    Google Scholar 

  31. Parsopoulos KE, Vrahatis MN (2010) Particle swarm optimization and intelligence: advances and applications. Information Science Reference-Imprint of IGI Publishing

  32. Choi S, Cha S, Tappert CC (2010) A survey of binary similarity and distance measures. Int J Syst Cybern Inform 8(1):43–48

    Google Scholar 

  33. Calinski T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat 3(1):1–27

    MathSciNet  MATH  Google Scholar 

  34. Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1(2):224–227

    Article  Google Scholar 

  35. Bandyopadhyay S, Maulik U (2001) Nonparametric genetic clustering: comparison of validity indices. IEEE Trans Syst Man Cybern, Part C, Appl Rev 31(1):120–125

    Article  Google Scholar 

  36. Bandyopadhyay S Artificial data sets for data mining, available in http://www.isical.ac.in/~sanghami/data.html

  37. UCI Repository of Machine Learning Databases retrieved from the World Wide Web: http://www.ics.uci.edu/~mlearn/MLRepository.html

  38. Shi C, Yuhui S (2011) Diversity control in particle swarm optimization, Paper presented at the IEEE Symposium on Swarm Intelligence (SIS), 11–15 April 2011

  39. Norouzzadeh M, Ahmadzadeh M, Palhang M (2011) LADPSO: using fuzzy logic to conduct PSO algorithm. Appl Intell 37(2):290–304

    Article  Google Scholar 

  40. Zhang W, Liu Y, Clerc M (2003) An adaptive PSO algorithm for reactive power optimization. In: 6th international conference on advances in power control, operation and management, Hong Kong, pp 302–307

    Google Scholar 

  41. García-Villoria A, Pastor R (2009) Introducing dynamic diversity into a discrete particle swarm optimization. Comput Oper Res 36(3):951–966. doi:10.1016/j.cor.2007.12.001

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. SCS lab, Computer Engineering Department, Electrical and Computer Engineering Faculty, Tarbiat Modares University, Tehran, Iran

    Hamid Masoud, Saeed Jalili & Seyed Mohammad Hossein Hasheminejad

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  1. Hamid Masoud
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  2. Saeed Jalili
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  3. Seyed Mohammad Hossein Hasheminejad
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Correspondence to Saeed Jalili.

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Masoud, H., Jalili, S. & Hasheminejad, S.M.H. Dynamic clustering using combinatorial particle swarm optimization. Appl Intell 38, 289–314 (2013). https://doi.org/10.1007/s10489-012-0373-9

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