Abstract
The present study proposes TLBO-PSO an integrated Teacher–Learning-Based Optimization (TLBO) and Particle Swarm Optimization (PSO) for optimum data clustering. TLBO-PSO algorithm searches through arbitrary datasets for appropriate cluster centroid and tries to find the global optima efficiently. The proposed TLBO-PSO is analyzed on a set of six benchmark datasets available at UCI machine learning repository. Experimental result shows that the proposed algorithm performs better than the other state-of-the-art clustering algorithms.
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References
Kant, S., Ansari, I.A.: An improved K means clustering with Atkinson index to classify liver patient dataset. Int. J. Syst. Assur. Eng. Manag. 7(1), 222–228 (2016)
Cornuéjols, A., Wemmert, C., Gançarski, P., Bennani, Y.: Collaborative clustering: why, when, what and how. Inf. Fusion 39, 81–95 (2018)
Han, X., Quan, L., Xiong, X., Almeter, M., Xiang, J., Lan, Y.: A novel data clustering algorithm based on modified gravitational search algorithm. Eng. Appl. Artif. Intell. 61, 1–7 (2017)
Prakash, J., Singh, P.K.: Particle swarm optimization with K-means for simultaneous feature selection and data clustering. In: 2015 Second International Conference Soft Computing Machine Intelligence, pp. 74–78 (2015)
Zhang, C., Ouyang, D., Ning, J.: An artificial bee colony approach for clustering. Expert Syst. Appl. 37(7) 4761–4767 (2010)
Kant, S., Mahara, T.: Merging user and item based collaborative filtering to alleviate data sparsity. Int. J. Syst. Assur. Eng. Manag. 1–7 (2016)
Kant, S., Mahara, T.: Nearest biclusters collaborative filtering framework with fusion. J. Comput. Sci. (2017)
Everitt, B.S., Landau, S., Leese, M., Stahl, D.: Cluster Anal. 14 (2011)
Xu, R., Ii, D.W.: Surv. Clust. Algorithms 16(3), 645–678 (2005)
Khatami, A., Mirghasemi, S., Khosravi, A., Lim, C.P., Nahavandi, S.: A new PSO-based approach to fire flame detection using K-medoids clustering. Expert Syst. Appl. 68, 69–80 (2017)
Hartigan, J.A.: Clust. algorithms. Wiley Publ. Appl. Stat. 1–351. 175 AD
Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31(8), 651–666 (2010)
Macqueen, J.: Some methods for classification and analysis of multivariate observations. Proc. Fifth Berkeley Symp. Math. Stat. Probab. 1(233), 281–297 (1967)
Peña, J., Lozano, J., Larrañaga, P.: An empirical comparison of four initialization methods for the K-means algorithm. Pattern Recognit. Lett. 20(10), 1027–1040 (1999)
Celebi, M.E., Kingravi, H.A., Vela, P.A.: A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst. Appl. 40(1), 200–210 (2013)
Maulik, U., Bandyopadhyay, S.: Genetic algorithm-based clustering technique. Pattern Recognit. 33, 1455–1465 (2000)
Chen, C.-Y., Ye, F.: Particle swarm optimization algorithm and its application to clustering analysis. In: 2004 IEEE Conference on Networking, Sensing Control, no. 1, pp. 789–794 (2004)
van der Merwe, D.W., Engelbrecht, A.P.: Data clustering using particle swarm optimization. In: 2003 Congress on Evolutionary Computation, CEC’03, pp. 215–220 (2003)
Selim, S.Z., Alsultan, K.: A simulated annealing algorithm for the clustering problem. Pattern Recognit. 24(10), 1003–1008 (1991)
Ozturk, C., Hancer, E., Karaboga, D.: Dynamic clustering with improved binary artificial bee colony algorithm. Appl. Soft Comput. J. 28, 69–80 (2015)
Banharnsakun, A.: A MapReduce-based artificial bee colony for large-scale data clustering. Pattern Recognit. Lett. 93, 78–84 (2017)
Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: An ant colony approach for clustering. Anal. Chim. Acta 509(2), 187–195 (2004)
Krishna, K., Narasimha Murty, M.: Genetic K-means algorithm. IEEE Trans. Syst. Man Cybern. Part B 29(3), 433–439 (1999)
Fan, S.S., Liang, Y., Zahara, E.: Hybrid simplex search and particle swarm optimization for the global optimization of multimodal functions. Eng. Optim. 36(4), 401–418 (2004)
Kao, Y.-T., Zahara, E., Kao, I.-W.: A hybridized approach to data clustering. Expert Syst. Appl. 34(3), 1754–1762 (2008)
Chuang, L.-Y., Hsiao, C.-J., Yang, C.-H.: Chaotic particle swarm optimization for data clustering. Expert Syst. Appl. 38(12), 14555–14563 (2011)
Chuanwen, J., Bompard, E.: A self-adaptive chaotic particle swarm algorithm for short term hydroelectric system scheduling in deregulated environment. Energy Convers. Manag. 46(17), 2689–2696 (2005)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS’95, pp. 39–43 (1995)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of 1998 IEEE World Congress on Evolutionary Computation. In: 1998 IEEE International Conference on Computational Intelligence, pp. 69–73 (1998)
Chen, W.N., et al.: Particle swarm optimization with an aging leader and challengers. IEEE Trans. Evol. Comput. 17(2), 241–258 (2013)
Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Des. 43(3), 303–315 (2011)
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Kushwaha, N., Pant, M. (2019). A Teaching–Learning-Based Particle Swarm Optimization for Data Clustering. In: Tanveer, M., Pachori, R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore. https://doi.org/10.1007/978-981-13-0923-6_19
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DOI: https://doi.org/10.1007/978-981-13-0923-6_19
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