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A hybrid bio-inspired algorithm and its application

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Abstract

Clustering is one of the attractive and major tasks in data mining that is used in many applications. It refers to group together data points, which are similar to one another based on some criteria. One of the efficient algorithms which applied on data clustering is particle swarm optimization (PSO) algorithm. However, PSO often leads to premature convergence and its performance is highly depended on parameter tuning and many efforts have been done to improve its performance in different ways. In order to improve the efficiency of the PSO on data clustering, it is hybridized with the big bang-big crunch algorithm (BB-BC) in this paper. In the proposed algorithm, namely PSO-BB-BC, PSO is used to explore the search space for finding the optimal centroids of the clusters. Whenever PSO loses its exploration, to prevent premature convergence, BB-BC algorithm is used to diversify the particles. The performance of the hybrid algorithm is compared with PSO, BB-BC and K-means algorithms using six benchmark datasets taken from the UCI machine learning repository. Experimental results show that the hybrid algorithm is superior to other test algorithms in all test datasets in terms of the quality of the clusters.

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References

  1. Jain A K (2010) Data clustering: 50 years beyond K-means. Pattern Recogn Lett 31(8):651–666

    Article  Google Scholar 

  2. Han J, Kamber M (2001) Data Mining: concepts and techniques, Academic Press

  3. Hruschka E R, Campello R J G B, de Castro L N (2006) Evolving clusters in gene-expression data. Inf Sci 176(13):1898– 1927

  4. Kerr G et al (2008) Techniques for clustering gene expression data. Comput Biol Med 38(3):283–293

    Article  Google Scholar 

  5. Wang Y-J, Lee H-S (2008) A clustering method to identify representative financial ratios. Inf Sci 178 (1):1087–1097

    Article  MATH  Google Scholar 

  6. Li J, Wang K, Xu L (2009) Chameleon based on clustering feature tree and its application in customer segmentation. Ann Oper Res 168(1):225–245

    Article  MATH  Google Scholar 

  7. Anaya-Sanchez H, Pons-Porrata A, Berlanga-Llavori R (2010) A document clustering algorithm for discovering and describing topics. Pattern Recog Lett 31(3):502–510

    Article  Google Scholar 

  8. Carullo M, Binaghi E, Gallo I (2009) An online document clustering technique for short web contents. Pattern Recog Lett 30(10):870–876

    Article  Google Scholar 

  9. Mahdavi M et al (2008) Novel meta-heuristic algorithms for clustering web documents. Appl Math Comput 201(1-2):441–451

    MathSciNet  MATH  Google Scholar 

  10. Friedman M et al (2007) Anomaly detection in web documents using crisp and fuzzy-based cosine clustering methodology. Inf Sci 177(2):467–475

    Article  Google Scholar 

  11. Moshtaghi M et al (2011) Clustering ellipses for anomaly detection. Pattern Recog 44(1):55–69

    Article  MATH  Google Scholar 

  12. Papajorgji P et al (2009) Clustering and classification algorithms in food and agricultural applications: a survey advances in modeling agricultural systems. Springer, pp 433–454

  13. Halberstadt W, Douglas T S (2008) Fuzzy clustering to detect tuberculous meningitis-associated hyperdensity in CT images. Comput Biol Med 38(2):165–170

    Article  Google Scholar 

  14. Liao L, Lin T, Li B (2008) MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach. Pattern Recog Lett 29(10):1580–1588

    Article  Google Scholar 

  15. Das S, Sil S (2009) Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm. Inf Sci 180(8):1237–1256

    Article  MathSciNet  Google Scholar 

  16. Yang S et al (2010) Evolutionary clustering based vector quantization and SPIHT coding for image compression. Pattern Recog Lett 31(13):1773–1780

    Article  Google Scholar 

  17. Kaur P, Soni A K, Gosain A (2013) RETRACTED: A robust kernelized intuitionistic fuzzy c-means clustering algorithm in segmentation of noisy medical images. Pattern Recog Lett 34(2):163–175

    Article  Google Scholar 

  18. Nguyen T D, Lee G (2012) Color image segmentation using tensor voting based color clustering. Pattern Recog Lett 33(2):605–614

    Article  Google Scholar 

  19. Wang L, Dong M (2012) Multi-level low-rank approximation-based spectral clustering for image segmentation. Pattern Recog Lett 33(16):2206–2215

    Article  Google Scholar 

  20. Aliguliyev R M (2009) Performance evaluation of density-based clustering methods. Inf Sci 179(20):3583–3602

    Article  Google Scholar 

  21. Tu Q et al Density-based hierarchical clustering for streaming data. Pattern Recog Lett 33(2):641–645

  22. Li C-Z, Xu Z-B, Luo T (2013) A heuristic hierarchical clustering based on multiple similarity measurements. Pattern Recog Lett 34(2):155–162

    Article  Google Scholar 

  23. Tasoulis S K, Tasoulis D K, Plagianakos V P (2013) Random direction divisive clustering. Pattern Recog Lett 34(2):131–139

    Article  MATH  Google Scholar 

  24. Hatamlou A (2012) In search of optimal centroids on data clustering using a binary search algorithm. Pattern Recog Lett 33(13):1756–1760

    Article  Google Scholar 

  25. Senthilnath J, Omkar S N, Mani V (2011) Clustering using firefly algorithm: performance study. Swarm and Evolutionary Computation 1(3):164–171

    Article  Google Scholar 

  26. Seyedali M, Andrew L The whale optimization algorithm. Adv Eng Softw 95(C):51–67

  27. 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

    Article  Google Scholar 

  28. Saatchi S, Hung C C (2005) Hybridization of the ant colony optimization with the k-means algorithm for clustering. Lecture Notes in Computer Science

  29. Menendez H D, Otero F E B, Camacho D (2016) Medoid-based clustering using ant colony optimization. Swarm Intelligence 10(2):123–145

    Article  Google Scholar 

  30. Hatamlou A, Hatamlou M PSOHS: an efficient two-stage approach for data clustering. Memetic Computing 5(2):155–161

  31. Hatamlou A (2014) Heart: a novel optimization algorithm for cluster analysis. Progress in Artificial Intelligence 2(2-3):167–173

    Article  Google Scholar 

  32. Serapiao A B S et al (2016) Combining K-Means and K-Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units. Appl Soft Comput 41:290–304

    Article  Google Scholar 

  33. Hatamlou A, Abdullah S, Nezamabadi-pour H (2011) Application of gravitational search algorithm on data clustering, rough sets and knowledge technology. Springer

  34. Hatamlou A, Abdullah S, Nezamabadi-pour H (2012) A combined approach for clustering based on K-means and gravitational search algorithms. Swarm Evol Comput 6(0):47–52

    Article  Google Scholar 

  35. Hatamlou A, Abdullah S, Othman Z (2011) Gravitational search algorithm with heuristic search for clustering problems 2011 3rd conference on data mining and optimization (DMO)

    Google Scholar 

  36. Hatamlou A, Hatamlou M (2013) Hybridization of the gravitational search algorithm and Big Bang-Big crunch algorithm for data clustering. Fundamenta Informaticae 126(1):319–333

    MathSciNet  Google Scholar 

  37. Mirjalili S, Jangir P, Saremi S (2016) Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl Intell:1–17

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

    MathSciNet  MATH  Google Scholar 

  39. Seyedali M, Seyed Mohammad M, Andrew L Grey Wolf optimizer. Adv Eng Softw 69:46–61

  40. Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222(0):175–184

    Article  MathSciNet  Google Scholar 

  41. Farahmandian M, Hatamlou A (2015) Solving optimization problems using black hole algorithm. J Adv Comput Sci Technol 4(1):68–74

    Article  Google Scholar 

  42. Hatamlou A, Abdullah S, Hatamlou M (2011) Data clustering using big bang-big crunch algorithm. Communications in Computer and Information Science pp 383–388

  43. Akay B, Karaboga D (2012) A modified Artificial Bee Colony algorithm for real-parameter optimization. Inf Sci 192 (0):120– 142

    Article  Google Scholar 

  44. Yeh W-C (2012) Novel swarm optimization for mining classification rules on thyroid gland data. Inf Sci 197 (0):65–76

    Article  Google Scholar 

  45. Connolly J-F, Granger E, Sabourin R (2012) An adaptive classification system for video-based face recognition. Inf Sci 192(0):50–70

    Article  Google Scholar 

  46. Manoj V J, Elias E (2012) Artificial bee colony algorithm for the design of multiplier-less nonuniform filter bank transmultiplexer. Inf Sci 192(0):193–203

    Article  Google Scholar 

  47. Christmas J et al (2011) Ant colony optimisation to identify genetic variant association with type 2 diabetes. Inf Sci 181(9):1609–1622

    Article  Google Scholar 

  48. Bouyer A, Hatamlou A, Abdullah A H (2010) An optimized clustering algorithm using genetic algorithm and rough set theory based on kohonen self organizing map. International Journal of Computer Science and Information Security 8(1):39–44

    Google Scholar 

  49. Bouyer A, Hatamlou A (2014) Hybridization of the LEACH Protocol with Penalized Fuzzy C-Means (PFCM) and Self-Organization Map (SOM) Algorithms for decreasing energy in wireless sensor networks. International Journal of Business Data Communications and Networking (IJBDCN) 10(3):46–64

    Article  Google Scholar 

  50. Hatamlou A, Ghaniyarlou E (2016) Solving knapsack problems using heart algorithm. Int J Artif Intell Soft Comput 5(1):285–293

    Article  Google Scholar 

  51. Mohrechi K, Hatamlou A (2015) Locating optimal places for emergency medical centers using artificial bee colony algorithm. J Adv Comput Res 6(1):115–124

    Google Scholar 

  52. Mohammadi P, Hatamlou A, Masdari M (2013) A comparative study on remote tracking of Parkinsons disease progression using data mining methods. arXiv:1312.2140

  53. Kennedy J, Eberhart R (1995) Particle swarm optimization Proceedings of the IEEE international conference on neural networks

    Google Scholar 

  54. Alinia Ahandani M et al Hybrid particle swarm optimization transplanted into a hyper-heuristic structure for solving examination timetabling problem. Swarm Evol Comput 7(0):21–34

  55. Khan S A, Engelbrecht A P (2012) A fuzzy particle swarm optimization algorithm for computer communication network topology design. Appl Intell 36(1):161–177

    Article  Google Scholar 

  56. Gao H et al Particle swarm optimization based on intermediate disturbance strategy algorithm and its application in multi-threshold image segmentation. Inf Sci 250(0):82–112

  57. Navalertporn T, Afzulpurkar N V Optimization of tile manufacturing process using particle swarm optimization. Swarm Evol Comput 1(2):97–109

  58. Papa J O P, Fonseca L M G, de Carvalho L A S Projections Onto Convex Sets through Particle Swarm Optimization and its application for remote sensing image restoration. Pattern Recog Lett 31(13):1876–1886

  59. Perez C A et al Face and iris localization using templates designed by particle swarm optimization. Pattern Recog Lett 31 (9):857– 868

  60. Suresh K, Kumarappan N Hybrid improved binary particle swarm optimization approach for generation maintenance scheduling problem. Swarm Evol Comput 9(0):69–89

  61. Erol O K, Eksin I (2006) A new optimization method: Big Bang-Big Crunch. Adv Eng Softw 37(2):106–111

    Article  Google Scholar 

  62. Kaveh A, Talatahari S (2009) Size optimization of space trusses using Big Bang-Big Crunch algorithm. Comput Struct 87 (17–18):1129–1140

    Article  Google Scholar 

  63. Tang H et al (2010) Big Bang-Big Crunch optimization for parameter estimation in structural systems. Mech Syst Signal Process 24(8):2888–2897

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Computer Science, Khoy Branch, Islamic Azad University, Khoy, Iran

    Abdolreza Hatamlou

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Hatamlou, A. A hybrid bio-inspired algorithm and its application. Appl Intell 47, 1059–1067 (2017). https://doi.org/10.1007/s10489-017-0951-y

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