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An improved bacterial colony optimization using opposition-based learning for data clustering

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Abstract

Data clustering is a technique for dividing data objects into groups based on their similarity. K-means is a simple, effective algorithm for clustering. But, k-means tends to converge to local optima and depends on the cluster’s initial values. To address the shortcomings of the k-means algorithm, many nature-inspired techniques have been used. This paper is offered an improved version of bacterial colony optimization (BCO) based on opposition-based learning (OBL) algorithm called OBL + BCO for data clustering. An OBL is used to increase the speed of the convergence rate and searching ability of BCO by computing the opposite solution to the present solution. The strength of the proposed data clustering technique is evaluated using several well-known UCI benchmark datasets. Different performance measures are considered to analyze the strength of the proposed OBL + BCO such as Rand index, Jaccard index, Beta index, Distance index, Objective values, and computational time. The experimental results demonstrated that the proposed OBL + BCO data clustering technique outperformed other data clustering techniques.

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Data availability

The used data is available on https://archive.ics.uci.edu/ml/index.php

References

  1. Babu, S.S., Jayasudha, K.: A survey of nature-inspired algorithm for partitional data clustering. in J. Phys.: Conf. Ser. (2020).

  2. Majhi, S.K., Biswal, S.: Optimal cluster analysis using hybrid K-means and ant lion optimizer. Karbala Int. J. Modern Sci. 4(4), 347–360 (2018)

    Article  Google Scholar 

  3. Revathi, J., Eswaramurthy, V., Padmavathi, P.: Bacterial colony optimization for data clustering. In 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE (2019).

  4. Revathi, J., Eswaramurthy, V., Padmavathi, P.: Hybrid data clustering approaches using bacterial colony optimization and k-means. IOP Conf. Ser.: Mater. Sci. Eng. 1070, 012064 (2021)

    Article  Google Scholar 

  5. Vijayakumari, K., Deepa, V.B.: Fuzzy means C-hybrid with fuzzy bacterial colony optimization. In: Sengodan, T., Murugappan, M., Misra, S. (eds.) Advances in electrical and computer technologies: select proceedings of ICAECT 2020. Springer Singapore (2021)

    Google Scholar 

  6. Tamilarisi, K., Gogulkumar, M., Velusamy, K.: Data clustering using bacterial colony optimization with particle swarm optimization. In 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE (2021).

  7. Sahoo, G.: A two-step artificial bee colony algorithm for clustering. Neural Comput. Appl. 28(3), 537–551 (2017)

    Article  Google Scholar 

  8. Likas, A., Vlassis, N., Verbeek, J.J.: The global k-means clustering algorithm. Pattern Recogn. 36(2), 451–461 (2003)

    Article  Google Scholar 

  9. Hruschka, E.R., Ebecken, N.F.: A genetic algorithm for cluster analysis. Intelligent Data Analysis 7(1), 15–25 (2003)

    Article  Google Scholar 

  10. Van der Merwe, D., Engelbrecht, A.P.: Data clustering using particle swarm optimization. In The 2003 congress on evolutionary computation, 2003. CEC’03. IEEE (2003).

  11. Chuang, L.-Y., Hsiao, C.-J., Yang, C.-H.: Chaotic particle swarm optimization for data clustering. Expert Syst. Appl. 38(12), 14555–14563 (2011)

    Article  Google Scholar 

  12. Shukla, U.P., Nanda, S.J.: Parallel social spider clustering algorithm for high dimensional datasets. Eng. Appl. Artif. Intell. 56, 75–90 (2016)

    Article  Google Scholar 

  13. Ahmadi, R., Ekbatanifard, G., Bayat, P.: A modified Grey Wolf optimizer based data clustering algorithm. Appl. Artif. Intell. 35(1), 63–79 (2021)

    Article  Google Scholar 

  14. Kuwil, F.H., et al.: A novel data clustering algorithm based on gravity center methodology. Expert Syst. Appl. 156, 113435 (2020)

    Article  Google Scholar 

  15. Talaei, K., Rahati, A., Idoumghar, L.: A novel harmony search algorithm and its application to data clustering. Appl. Soft Comput. 92, 106273 (2020)

    Article  Google Scholar 

  16. Gao, X., et al.: A hybrid optimization method of harmony search and opposition-based learning. Eng. Optim. 44(8), 895–914 (2012)

    Article  Google Scholar 

  17. Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.: Opposition-based differential evolution. IEEE Trans. Evol. Comput. 12(1), 64–79 (2008)

    Article  Google Scholar 

  18. Han, L., He, X.: A novel opposition-based particle swarm optimization for noisy problems. In Third international conference on natural computation (ICNC 2007). IEEE (2007).

  19. Wang, H. et al.: Opposition-based particle swarm algorithm with Cauchy mutation. In 2007 IEEE congress on evolutionary computation. IEEE (2007).

  20. Ahandani, M.A., Alavi-Rad, H.: Opposition-based learning in the shuffled differential evolution algorithm. Soft. Comput. 16(8), 1303–1337 (2012)

    Article  Google Scholar 

  21. Kaucic, M.: A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization. J. Global Optim. 55(1), 165–188 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  22. Jordehi, A.R.: Particle swarm optimisation with opposition learning-based strategy: an efficient optimisation algorithm for day-ahead scheduling and reconfiguration in active distribution systems. Soft Comput, 1–18 (2020).

  23. Cho, P.P.W., Nyunt, T.T.S.: Data clustering based on modified differential evolution and quasi-opposition-based learning. Intell. Eng. Syst. 13(6), 168–178 (2020)

    Article  Google Scholar 

  24. Abraham, A., Das, S., Roy, S.: Swarm intelligence algorithms for data clustering. In: Soft computing for knowledge discovery and data mining, pp. 279–313. Springer (2008)

    Chapter  Google Scholar 

  25. Niu, B., Wang, H.: Bacterial colony optimization. Discrete Dyn. Nat. Soc. 2012, 1–28 (2012)

    MathSciNet  MATH  Google Scholar 

  26. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22(3), 52–67 (2002)

    Article  Google Scholar 

  27. Muller, S.D., et al.: Optimization based on bacterial chemotaxis. IEEE Trans. Evol. Comput. 6(1), 16–29 (2002)

    Article  Google Scholar 

  28. Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06). IEEE (2005).

  29. Verma, O.P., Aggarwal, D., Patodi, T.: Opposition and dimensional based modified firefly algorithm. Expert Syst. Appl. 44, 168–176 (2016)

    Article  Google Scholar 

  30. Xiong, G., et al.: Oppositional brain storm optimization for fault section location in distribution networks. In: Brain storm optimization algorithms, pp. 61–77. Springer (2019)

    Chapter  Google Scholar 

  31. Ewees, A.A., Elaziz, M.A., Oliva, D.: A new multi-objective optimization algorithm combined with opposition-based learning. Expert Syst. App. 165, 113844 (2021)

    Article  Google Scholar 

  32. Muthusamy, H., et al.: An improved elephant herding optimization using sine–cosine mechanism and opposition based learning for global optimization problems. Expert Syst. Appl. 172, 114607 (2021)

    Article  Google Scholar 

  33. Zhang, Z., et al.: A hybrid max-min ant system by levy flight and opposition-based learning. Int. J. Pattern Recogn. Artif. Intell. 35, 2151013 (2021)

    Article  Google Scholar 

  34. Kalaiselvi, K., Velusamy, K., Gomathi, C.: Financial prediction using back propagation neural networks with opposition based learning. J. Phys.: Conf. Ser.. 1142, 012008 (2018)

    Google Scholar 

  35. Bairathi, D., Gopalani, D.: Opposition-based sine cosine algorithm (OSCA) for training feed-forward neural networks. In 2017 13th international conference on signal-image technology & internet-based systems (SITIS). IEEE (2017).

  36. Jordehi, A.R.: Particle swarm optimisation with opposition learning-based strategy: an efficient optimisation algorithm for day-ahead scheduling and reconfiguration in active distribution systems. Soft. Comput. 24(24), 18573–18590 (2020)

    Article  Google Scholar 

  37. Ventresca, M., Tizhoosh, H.R.: A diversity maintaining population-based incremental learning algorithm. Inf. Sci. 178(21), 4038–4056 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  38. Shelokar, P., Jayaraman, V.K., Kulkarni, B.D.: An ant colony approach for clustering. Anal. Chim. Acta 509(2), 187–195 (2004)

    Article  Google Scholar 

  39. De Falco, I., Cioppa, A.D., Tarantino, E.: Facing classification problems with particle swarm optimization. Appl. Soft Comput. 7(3), 652–658 (2007)

    Article  Google Scholar 

  40. Wang, L., et al.: A new chaotic starling particle swarm optimization algorithm for clustering problems. Math. Prob. Eng. 2018, 1–14 (2018)

    Google Scholar 

  41. Tran, D.C., Wu, Z., Deng, C.: An improved approach of particle swarm optimization and application in data clustering. Intell. Data Analy. 19(5), 1049–1070 (2015)

    Article  Google Scholar 

  42. Wan, M., et al.: Data clustering using bacterial foraging optimization. J. Intell. Inform. Syst. 38(2), 321–341 (2012)

    Article  Google Scholar 

  43. Ye, F.-L., et al.: Incorporating particle swarm optimization into improved bacterial foraging optimization algorithm applied to classify imbalanced data. Symmetry 12(2), 229 (2020)

    Article  Google Scholar 

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

    Article  Google Scholar 

  45. Borgelt, C.: Objective functions for fuzzy clustering. In: Computational intelligence in intelligent data analysis, pp. 3–16. Springer (2013)

    Chapter  Google Scholar 

  46. Kanungo, T., et al.: An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)

    Article  Google Scholar 

  47. Kennedy, J., Eberhart, R.: Particle swarm optimization. In Proceedings of ICNN’95-international conference on neural networks. IEEE (1995).

  48. Kwedlo, W.: A clustering method combining differential evolution with the K-means algorithm. Pattern Recogn. Lett. 32(12), 1613–1621 (2011)

    Article  Google Scholar 

  49. Cura, T.: A particle swarm optimization approach to clustering. Expert Syst. Appl. 39(1), 1582–1588 (2012)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Kristu Jayanti College (Autonomous), Bangalore, India

    V. S. Prakash, V. Vinothina & K. Kalaiselvi

  2. Department of Computer Science, Sri Vasavi College, Erode, Tamil Nadu, India

    K. Velusamy

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  1. V. S. Prakash
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  2. V. Vinothina
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  3. K. Kalaiselvi
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  4. K. Velusamy
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To the algorithms and development, as well as the paper, were all contributed by all authors. The final manuscript has been read and approved by all authors.

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Correspondence to V. S. Prakash.

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Prakash, V.S., Vinothina, V., Kalaiselvi, K. et al. An improved bacterial colony optimization using opposition-based learning for data clustering. Cluster Comput 25, 4009–4025 (2022). https://doi.org/10.1007/s10586-022-03633-z

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