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Memetic differential evolution methods for clustering problems
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DOI：10.1016/j.patcog.2021.107849.png)
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The Euclidean Minimum Sum-of-Squares Clustering ( MSSC ) is one of the most important models for the clustering problem. Due to its NP-hardness, the problem continues to receive much attention in the scientific literature and several heuristic procedures have been proposed. Recent research has been devoted to the improvement of the classical K-MEANS algorithm, either by suitably selecting its starting configuration or by using it as a local search method within a global optimization algorithm. This paper follows this last approach by proposing a new implementation of a Memetic Differential Evolution ( MDE ) algorithm specifically designed for the MSSC problem and based on the repeated execution of K-MEANS from selected configurations. In this paper we describe how to adapt MDE to the clustering problem and we show, through a vast set of numerical experiments, that the proposed method has very good quality, measured in terms of the minimization of the objective function, as well as a very good efficiency, measured in the number of calls to the local optimization routine, with respect to state of the art methods. (c) 2021 Elsevier Ltd. All rights reserved.
Keyword:
Global optimization
Clustering
Minimum sum-of-squares
Hybrid genetic algorithm
K-MEANS
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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分享收藏J-MEANS: a new local search heuristic for minimum sum of squares clustering.png)
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分享收藏How much can k-means be improved by using better initialization and repeats?通过使用更好的初始化和重复，k均值可以提高多少？PATTERN RECOGNITION
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