Abstract
Clustering is grouping the similar data points in the clusters. Large-scale data grouping has discovered wide applications in many fields, particularly in big data analytics. Traditional clustering algorithms do not explore and exploit all feasible solutions of clustering. Artificial Bee Colony algorithm (ABC) is a metaheuristic algorithm applied for clustering. ABC suffers from slow convergence. Hence, Improved ABC (IABC) is used for experimentation. UCI datasets—wine and seed are modified to large scale and used for experimentation. Experimental results show that IABC give the quality clusters than ABC and K-mean for large-scale dataset. ABC gives the better clusters than K-mean.
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Gaikwad, M.R., Umbarkar, A.J., Bamane, S.S. (2020). Large-Scale Data Clustering Using Improved Artificial Bee Colony Algorithm. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Systems and Sustainability. Advances in Intelligent Systems and Computing, vol 1077. Springer, Singapore. https://doi.org/10.1007/978-981-15-0936-0_50
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DOI: https://doi.org/10.1007/978-981-15-0936-0_50
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