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Continuous greedy randomized adaptive search procedure for data clustering
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DOI：10.1016/j.asoc.2018.07.031.png)
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摘要
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Cluster analysis is an unsupervised machine learning task that aims at finding the most similar groups of objects, given a prespecified similarity measure. When modeled as an optimization problem, clustering problems generally are NP-hard. Therefore, the use of metaheuristic approaches appears to be a promising alternative. In this paper, a continuous greedy randomized adaptive search procedure (C-GRASP) approach is proposed to solve a partitional clustering problem that aims at minimizing the intra-cluster distances. Computational experiments carried out on existing databases showed that the results obtained by the proposed algorithm was, on average, superior to those found by other well-known metaheuristics, as well as to those achieved by state-of-the-art algorithms from the literature. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Data clustering
Pattern recognition
Continuous GRASP
Optimization
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6.6
论文数:
1.4W
被引数:
4.8W
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