Abstract
Clustering is an essential data mining tool for analyzing big data. In this article, an overview of literature methods is undertaken. Following this study, a new algorithm called BSO-CLARA is proposed for clustering large data sets. It is based on bee behavior and k-medoids partitioning. Criteria like effectiveness, eficiency, scalability and control of noise and outliers are discussed for the new method and compared to those of the previous techniques. Experimental results show that BSO-CLARA is more effective and more efficient than PAM, CLARA and CLARANS, the well-known partitioning algorithms but also CLAM, a recent algorithm found in the literature.
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Aboubi, Y., Drias, H., Kamel, N. (2015). BSO-CLARA: Bees Swarm Optimization for Clustering LARge Applications. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_17
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DOI: https://doi.org/10.1007/978-3-319-26832-3_17
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