首页|A simulated annealing algorithm with a dual perturbation method for clustering
A simulated annealing algorithm with a dual perturbation method for clustering
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NSTL
Elsevier
Clustering is a powerful tool in exploratory data analysis that partitions a set of objects into clusters with the goal of maximizing the similarity of objects within each cluster. Due to the tendency of clustering algorithms to find suboptimal partitions of data, the approximation method Simulated Annealing (SA) has been used to search for near-optimal partitions. However, existing SA-based partitional clustering algorithms still settle to local optima. We propose a new SA-based clustering algorithm, the Simulated Annealing with Gaussian Mutation and Distortion Equalization algorithm (SAGMDE), which uses two perturbation methods to allow for both large and small perturbations in solutions. Our experiments on a diverse collection of data sets show that SAGMDE performs more consistently and yields better results than existing SA clustering algorithms in terms of cluster quality while maintaining a reasonable runtime. Finally, we use generative art as a visualization tool to compare various partitional clustering algorithms. (C) 2020 Elsevier Ltd. All rights reserved.
Partitional clusteringSimulated annealingSum of squared error criterionK-means
Lee, Julian、Perkins, David
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Pingry Sch, 131 Martinsville Rd, Basking Ridge, NJ 07920 USA
Hamilton Coll, Taylor Sci Bldg,198 Coll Hill Rd, Clinton, NY 13323 USA
NSTL
Elsevier
