WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis

Year 2022, Volume: 25 Issue: 1, 65 - 73, 01.03.2022

Mohammed Hussein Ibrahım

https://doi.org/10.2339/politeknik.689384

Cited By: 4

Abstract

Data clustering is an unsupervised classification method used to classify unlabeled objects into clusters. The clustering is performed by partitioning clustering, hierarchical clustering, fuzzy clustering, and density-based clustering methods. However, the center of the clusters is updated according to local searches with these traditional methods, and finding the best clusters center affects the clustering performance positively. In this study, a variant bat algorithm called weight-based bat algorithm (WBBA) is proposed and the proposed WBBA hybridized with the k-means clustering method (WBBA-KM) to determine the optimal centers of the clusters. The performance of the proposed WBBA-KM has been evaluated by using six different benchmark datasets from the UCI repository and the obtained results are compared with FCM, IFCM, KFCM, KIFCM, PSO-IFCM, GA-IFCM, ABC-IFCM, PSO-KIFCM, GA-KIFCM, ABC-KIFCM, and BA-KM clustering methods in the literature. According to the experimental results, the proposed WBBA-KM clustering method performed better performance from all other clustering methods in 4 of 6 benchmark datasets and achieved better performance from the BA-KM clustering method in all benchmark datasets.

Keywords

Bat algorithm, Cluster analysis, Optimization algorithms, Unsupervised classification

References

• 1. Han, J., Kamber, M., Pei, J., "Data mining concepts and techniques third edition", The Morgan Kaufmann Series in Data Management Systems, 83-124 (2011).
• 2. Ngo, T., "Data mining: practical machine learning tools and technique", by ian h. witten, eibe frank, mark a. hell, ACM Sigsoft Software Engineering Notes 36(5): 51-52, (2011).
• 3. Zhao, Q., "Cluster validity in clustering methods", Publications of the University of Eastern Finland (2012).
• 4. Nagpal, A., Jatain, A., Gaur, D., "Review based on data clustering algorithms", In: 2013 IEEE Conference on Information & Communication Technologies, 298-303, (2013).
• 5. Halkidi, M., Batistakis, Y., Vazirgiannis, M., "On clustering validation techniques", Journal of intelligent information systems, 17(2-3): 107-145, (2001).
• 6. Gulati, H., Singh, P., "Clustering techniques in data mining: A comparison", In: 2015 2nd international conference on computing for sustainable global development (INDIACom), 410-415, (2015).
• 7. Tang, R., Fong, S., Yang, X.-S., Deb, S., "Integrating nature-inspired optimization algorithms to K-means clustering", In: Seventh International Conference on Digital Information Management (ICDIM 2012), 116-123, (2012).
• 8. Gupta, M.D., "Socio-economic status and clustering of child deaths in rural Punjab", Population Studies, 51(2): 191-202, (1997).
• 9. El-Hamdouchi, A., Willett, P., "Hierarchic document classification using Ward's clustering method", In: Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval, 149-156, (1986).
• 10. Wong, W.-C., Fu, A.W.-C., "Incremental document clustering for web page classification", In: Enabling Society with Information Technology, 101-110, Springer, (2002).
• 11. Ebrahimzadeh, A., Addeh, J., Rahmani, Z., "Control chart pattern recognition using K-MICA clustering and neural networks", ISA transactions 51(1): 111-119, (2012).
• 12. Yu, J., Wu, J., Sarwat, M., "Geospark: A cluster computing framework for processing large-scale spatial data", In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, 70, (2015).
• 13. Uslan, V., Bucak, I., "Microarray image segmentation using clustering methods", Mathematical and Computational Applications, 15(2): 240-247, (2010).
• 14. Kuo, R., Lin, T., Zulvia, F.E., Tsai, C., "A hybrid metaheuristic and kernel intuitionistic fuzzy c-means algorithm for cluster analysis", Applied Soft Computing, 67, 299-308, (2018).
• 15. Tripathi, A.K., Sharma, K., Bala, M., "Dynamic frequency based parallel k-bat algorithm for massive data clustering (DFBPKBA)", International Journal of System Assurance Engineering and Management, 9(4): 866-874, (2018).
• 16. Maulik, U., Bandyopadhyay, S., "Genetic algorithm-based clustering technique", Pattern recognition, 33(9): 1455-1465, (2000).
• 17. Kalyani, S., Swarup, K.S., "Particle swarm optimization based K-means clustering approach for security assessment in power systems", Expert systems with applications, 38(9): 10839-10846, (2011).
• 18. Karaboga, D., Ozturk, C., "A novel clustering approach: Artificial Bee Colony (ABC) algorithm", Applied soft computing,11(1): 652-657, (2011).
• 19. Han, J., Pei, J., Kamber, M., "Data mining: concepts and techniques", Elsevier, (2011)
• 20. Fathian, M., Amiri, B., Maroosi, A., "Application of honey-bee mating optimization algorithm on clustering", Applied Mathematics and Computation, 190(2): 1502-1513, (2007).
• 21. Giancarlo, R., Bosco, G.L., Pinello, L., "Distance functions, clustering algorithms and microarray data analysis", In: International Conference on Learning and Intelligent Optimization, 125-138, (2010).
• 22. Hämäläinen, J., Jauhiainen, S., Kärkkäinen, T., "Comparison of internal clustering validation indices for prototype-based clustering" Algorithms, 10(3): 105, (2017).
• 23. Charrad, M., Ghazzali, N., Boiteux, V., Niknafs, A., "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set| Charrad|", Journal of Statistical Software, (2014)
• 24. Yang, X.-S., "A new metaheuristic bat-inspired algorithm", In: Nature inspired cooperative strategies for optimization (NICSO 2010), 65-74, Springer, (2010).
• 25. Yılmaz, S., Küçüksille, E.U., "A new modification approach on bat algorithm for solving optimization problems", Applied Soft Computing, 28, 259-275, (2015).