Improved SOSK-Means Automatic Clustering Algorithm with a Three-Part Mutualism Phase and Random Weighted Reflection Coefficient for High-Dimensional Datasets
Abstract:Automatic clustering problems require clustering algorithms to automatically estimate the number of clusters in a dataset. However, the classical K-means requires the specification of the required number of clusters a priori. To address this problem, metaheuristic algorithms are hybridized with K-means to extend the capacity of K-means in handling automatic clustering problems. In this study, we proposed an improved version of an existing hybridization of the classical symbiotic organisms search algorithm with… Show more
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