A simulated annealing algorithm with a dual perturbation method for clustering

•Existing partitional clustering algorithms still settle upon local optima.•We propose a new simulated annealing algorithm with two perturbation methods.•We compare our algorithm with existing simulated annealing clustering algorithms.•We show our new algorithm produces clusters of higher quality mo...

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Veröffentlicht in:Pattern recognition Jg. 112; S. 107713
Hauptverfasser: Lee, Julian, Perkins, David
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.04.2021
Schlagworte:
K-means
Partitional clustering
Simulated annealing
Sum of squared error criterion
K-means
Sum of squared error criterion
Partitional clustering
Simulated annealing
ISSN:0031-3203, 1873-5142
Online-Zugang:Volltext
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Zusammenfassung:•Existing partitional clustering algorithms still settle upon local optima.•We propose a new simulated annealing algorithm with two perturbation methods.•We compare our algorithm with existing simulated annealing clustering algorithms.•We show our new algorithm produces clusters of higher quality more consistently. 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.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2020.107713