Cluster ensembles: A survey of approaches with recent extensions and applications |
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| Affiliation: | 1. Lab. MIA, Univ. La Rochelle, France;2. Movidius, Romania;3. Visual Signal Analysis and Processing (VSAP) Research Center, Department of Electrical and Computer Engineering, Khalifa University, United Arab Emirates;1. Faculty of Computing, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia;2. Department of Computer Engineering, Islamic Azad University, Sari Branch, Sari, Iran;3. Computer Engineering Department, Iran University of Science and Technology, 1684613114 Narmak, Tehran, Iran;1. Department of Automation, Xiamen University, China;2. School of Computing and Information Sciences, Florida International University, USA;3. Department of Computer Science, Aalto University, Finland;4. School of Computer Science, Nanjing University of Posts and Telecommunications, China;1. College of Science and Technology, Ningbo University, 315211 Ningbo, China;2. Department of Computer Science and Technology, Shanghai University, 200444 Shanghai, China;3. College of Computer Science and Technology, Taiyuan University of Technology, 030024 Taiyuan, China;4. School of Computer Science and Technology, Shanghai University of Electric Power, 200090 Shanghai, China |
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| Abstract: | Cluster ensembles have been shown to be better than any standard clustering algorithm at improving accuracy and robustness across different data collections. This meta-learning formalism also helps users to overcome the dilemma of selecting an appropriate technique and the corresponding parameters, given a set of data to be investigated. Almost two decades after the first publication of a kind, the method has proven effective for many problem domains, especially microarray data analysis and its down-streaming applications. Recently, it has been greatly extended both in terms of theoretical modelling and deployment to problem solving. The survey attempts to match this emerging attention with the provision of fundamental basis and theoretical details of state-of-the-art methods found in the present literature. It yields the ranges of ensemble generation strategies, summarization and representation of ensemble members, as well as the topic of consensus clustering. This review also includes different applications and extensions of cluster ensemble, with several research issues and challenges being highlighted. |
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| Keywords: | Data clustering Cluster ensemble Theoretical extension Domain specific application |
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