Open Access

ARTICLE

Information Theoretic Weighted Fuzzy Clustering Ensemble

by Yixuan Wang¹, Liping Yuan^2,3, Harish Garg⁴, Ali Bagherinia⁵, Parvïn Hamïd^6,7,8,*, Kim-Hung Pho⁹, Zulkefli Mansor¹⁰

1 Department of Electronic Information Engineering, School of Information Engineering, Wuhan University of Technology, Wuhan, China
2 School of Information Engineering, Wuhan University of Technology, Wuhan, China
3 School of Information Engineering, Wuhan Huaxia University of Technology, Wuhan, China
4 School of Mathematics, Thapar Institute of Engineering and Technology, Deemed University, Patiala, Punjab, India
5 Department of Computer Science, Dehdasht Branch, Islamic Azad University, Dehdasht, Iran
6 Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam
7 Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Vietnam
8 Department of Computer Science, Nourabad Mamasani Branch, Islamic Azad University, Mamasani, Iran
9 Fractional Calculus, Optimization and Algebra Research Group, Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
10 Fakulti Teknologi dan Sains Maklumat Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia

* Corresponding Author: Parvïn Hamïd. Email:

Computers, Materials & Continua 2021, 67(1), 369-392. https://doi.org/10.32604/cmc.2021.012850

Received 14 July 2020; Accepted 19 August 2020; Issue published 12 January 2021

Abstract

In order to improve performance and robustness of clustering, it is proposed to generate and aggregate a number of primary clusters via clustering ensemble technique. Fuzzy clustering ensemble approaches attempt to improve the performance of fuzzy clustering tasks. However, in these approaches, cluster (or clustering) reliability has not paid much attention to. Ignoring cluster (or clustering) reliability makes these approaches weak in dealing with low-quality base clustering methods. In this paper, we have utilized cluster unreliability estimation and local weighting strategy to propose a new fuzzy clustering ensemble method which has introduced Reliability Based weighted co-association matrix Fuzzy C-Means (RBFCM), Reliability Based Graph Partitioning (RBGP) and Reliability Based Hyper Clustering (RBHC) as three new fuzzy clustering consensus functions. Our fuzzy clustering ensemble approach works based on fuzzy cluster unreliability estimation. Cluster unreliability is estimated according to an entropic criterion using the cluster labels in the entire ensemble. To do so, the new metric is defined to estimate the fuzzy cluster unreliability; then, the reliability value of any cluster is determined using a Reliability Driven Cluster Indicator (RDCI). The time complexities of RBHC and RBGP are linearly proportional with the number of data objects. Performance and robustness of the proposed method are experimentally evaluated for some benchmark datasets. The experimental results demonstrate efficiency and suitability of the proposed method.

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Keywords

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