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• W3166121878 abstract "Clustering is one of the basic tasks in machine learning and data mining. Euclidean minimum-sum-of-squares clustering problem is probably the most common clustering model. It consists in finding k cluster centers or representatives so as the sum of squared Euclidean distances from a set of points and their closest centers is minimized. The problem is known to be nonconvex and NP-hard even in the planner case. In this paper, we propose a new DC programming approach to the problem. First, we cast the original nonconvex problem as a continuous optimization problem with the objective function represented as a DC function. We then devise a solution algorithm, resting upon the global optimality conditions and global search scheme for DC minimization problems proposed by A.S. Strekalovsky. We implement the developed algorithm and compare it with k-means clustering algorithms on generated datasets." @default.
• W3166121878 created "2021-06-22" @default.
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• W3166121878 date "2021-01-01" @default.
• W3166121878 modified "2023-10-16" @default.
• W3166121878 title "K-Means Clustering via a Nonconvex Optimization Approach" @default.
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• W3166121878 doi "https://doi.org/10.1007/978-3-030-77876-7_31" @default.
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