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Soft Computing
Published in: Soft Computing 20/2020

20-08-2020 | Foundations

Multiple clustering and selecting algorithms with combining strategy for selective clustering ensemble

Authors: Tinghuai Ma, Te Yu, Xiuge Wu, Jie Cao, Alia Al-Abdulkarim, Abdullah Al-Dhelaan, Mohammed Al-Dhelaan

Published in: Soft Computing | Issue 20/2020

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Abstract

Clustering ensemble can overcome the instability of clustering and improve clustering performance. With the rapid development of clustering ensemble, we find that not all clustering solutions are effective in their final result. In this paper, we focus on selection strategy in selective clustering ensemble. We propose a multiple clustering and selecting approach (MCAS), which is based on different original clustering solutions. Furthermore, we present two combining strategies, direct combining and clustering combining, to combine the solutions selected by MCAS. These combining strategies combine results of MCAS and get a more refined subset of solutions, compared with traditional selective clustering ensemble algorithms and single clustering and selecting algorithms. Experimental results on UCI machine learning datasets show that the algorithm that uses multiple clustering and selecting algorithms with combining strategy performs well on most datasets and outperforms most selective clustering ensemble algorithms.
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Metadata
Title
Multiple clustering and selecting algorithms with combining strategy for selective clustering ensemble
Authors
Tinghuai Ma
Te Yu
Xiuge Wu
Jie Cao
Alia Al-Abdulkarim
Abdullah Al-Dhelaan
Mohammed Al-Dhelaan
Publication date
20-08-2020
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 20/2020
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-020-05264-1

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