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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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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