Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6864770 | Neurocomputing | 2018 | 13 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
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Artificial Intelligence
Authors
Jin Liu, Tuan D. Pham, Hong Yan, Zhizhen Liang,