Journal
ADVANCES IN DATA ANALYSIS AND CLASSIFICATION
Volume 2, Issue 3, Pages 211-225Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11634-008-0032-5
Keywords
Multi-objective optimization; Clustering; Fuzzy C-means; Microarray; Gene expression data
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Funding
- Centre National de la Recherche Scientifique
- Institut National de la Sante et de la Recherche Medicale
- Universite Louis Pasteur de Strasbourg
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In this paper, we use the Fuzzy C-means method for clustering 3-way gene expression data via optimization of multiple objectives. A reformulation of the total clustering criterion is used to obtain an expression which has fewer variables compared to the classical FCM criterion. This transformation allows the use of a direct global optimizer in constrast to the alternating search commonly used. Gene expression data from microarray technology is generally of high dimension. The problem of empty space is known for this kind of data. We propose in this paper a transformation allowing more contrast in distances between all pairs of data samples. This, hence, increases the likelihood of detecting group structure, if any, in high dimensional datasets.
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