Distribution-Based Cluster Structure Selection

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 su...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2017-11, Vol.47 (11), p.3554-3567
Main Authors: Yu, Zhiwen, Zhu, Xianjun, Wong, Hau-San, You, Jane, Zhang, Jun, Han, Guoqiang
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Cancer
Cluster ensemble
Clustering algorithms
clustering analysis
Clusters
Colleges & universities
Computer science
Datasets
expectation-maximization (EM)
Gaussian distribution
Gaussian mixture model (GMM)
Gene expression
graph cut
hypergraph
Parameter estimation
Performance enhancement
Performance evaluation
Periodic structures
Repositories
Similarity
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Summary: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.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2016.2569529