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Fuzzy mixed-prototype clustering algorithm for microarray data analysis
School of Computer Science, China University of Mining and Technology, Xuzhou, Jiangsu, China.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-4255-5130
Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong.
School of Computer Science, China University of Mining and Technology, Xuzhou, Jiangsu, China.
2017 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286Article in journal (Refereed) Epub ahead of print
Abstract [en]

Being motivated by combining the advantages of hyperplane-based pattern analysis and fuzzy clustering techniques, we present in this paper a fuzzy mix-prototype (FMP) clustering for microarray data analysis. By integrating spherical and hyper-planar cluster prototypes, the FMP is capable of capturing latent data models with both spherical and non-spherical geometric structures. Our contributions of the paper can be summarized into three folds: first, the objective function of the FMP is formulated. Second, an iterative solution which minimizes the objective function under given constraints is derived. Third, the effectiveness of the proposed FMP is demonstrated through experiments on yeast and leukemia data sets.

Place, publisher, year, edition, pages
Elsevier, 2017.
Keyword [en]
FMP, Microarray data analysis, Fuzzy clustering
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-141163DOI: 10.1016/j.neucom.2017.06.083OAI: oai:DiVA.org:liu-141163DiVA: diva2:1144031
Available from: 2017-09-25 Created: 2017-09-25 Last updated: 2017-10-13Bibliographically approved

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The full text will be freely available from 2019-09-20 16:11
Available from 2019-09-20 16:11

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Pham, Tuan

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  • nn-NB
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  • Other locale
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Output format
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