Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75685
Title: Distribution-based cluster structure selection
Authors: Yu, ZW 
Zhu, XJ
Wong, HS
You, JE 
Zhang, J
Han, GQ
Keywords: Cluster ensemble
Clustering analysis
Expectation-maximization (EM)
Gaussian mixture model (GMM)
Graph cut
Hypergraph
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on cybernetics, 2017, v. 47, no. 11, p. 3554-3567 How to cite?
Journal: IEEE transactions on cybernetics 
Abstract: The objective of cluster structure ensemble is to find a unified cluster structure from multiple cluster structures obtained from different datasets. Unfortunately, not all the cluster structures contribute to the unified cluster structure. This paper investigates the problem of how to select the suitable cluster structures in the ensemble which will be summarized to a more representative cluster structure. Specifically, the cluster structure is first represented by a mixture of Gaussian distributions, the parameters of which are estimated using the expectation-maximization algorithm. Then, several distribution-based distance functions are designed to evaluate the similarity between two cluster structures. Based on the similarity comparison results, we propose a new approach, which is referred to as the distribution-based cluster structure ensemble (DCSE) framework, to find the most representative unified cluster structure. We then design a new technique, the distribution-based cluster structure selection strategy (DCSSS), to select a subset of cluster structures. Finally, we propose using a distribution-based normalized hypergraph cut algorithm to generate the final result. In our experiments, a nonparametric test is adopted to evaluate the difference between DCSE and its competitors. We adopt 20 real-world datasets obtained from the University of California, Irvine and knowledge extraction based on evolutionary learning repositories, and a number of cancer gene expression profiles to evaluate the performance of the proposed methods. The experimental results show that: 1) DCSE works well on the real-world datasets and 2) DCSE based on DCSSS can further improve the performance of the algorithm.
URI: http://hdl.handle.net/10397/75685
ISSN: 2168-2267
EISSN: 2168-2275
DOI: 10.1109/TCYB.2016.2569529
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