An Improved Three-Way Clustering Based on Ensemble Strategy

Tingfeng Wu, Jiachen Fan and Pingxin Wang
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Tingfeng Wu: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Jiachen Fan: School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Pingxin Wang: School of Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China

Mathematics, 2022, vol. 10, issue 9, 1-22

Abstract: As a powerful data analysis technique, clustering plays an important role in data mining. Traditional hard clustering uses one set with a crisp boundary to represent a cluster, which cannot solve the problem of inaccurate decision-making caused by inaccurate information or insufficient data. In order to solve this problem, three-way clustering was presented to show the uncertainty information in the dataset by adding the concept of fringe region. In this paper, we present an improved three-way clustering algorithm based on an ensemble strategy. Different to the existing clustering ensemble methods by using various clustering algorithms to produce the base clustering results, the proposed algorithm randomly extracts a feature subset of samples and uses the traditional clustering algorithm to obtain the diverse base clustering results. Based on the base clustering results, labels matching is used to align all clustering results in a given order and voting method is used to obtain the core region and the fringe region of the three way clustering. The proposed algorithm can be applied on the top of any existing hard clustering algorithm to generate the base clustering results. As examples for demonstration, we apply the proposed algorithm on the top of K-means and spectral clustering, respectively. The experimental results show that the proposed algorithm is effective in revealing cluster structures.

Keywords: ensemble clustering; three-way decision; three-way clustering; voting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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