Fuzzy Gaussian Lasso clustering with application to cancer data

Miin-Shen Yang; Wajid Ali

doi:10.3934/mbe.2020014

Mathematical Biosciences and Engineering, 2020, 17(1): 250-265. doi: 10.3934/mbe.2020014. Research article Special Issues

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Fuzzy Gaussian Lasso clustering with application to cancer data

Miin-Shen Yang, Wajid Ali

Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li 32023, Taiwan

Received: , Accepted: , Published:

Special Issues: Applied Soft Computing

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Recently, Yang et al. (2019) proposed a fuzzy model-based Gaussian (F-MB-Gauss) clustering that combines a model-based Gaussian with fuzzy membership functions for clustering. In this paper, we further consider the F-MB-Gauss clustering with the least absolute shrinkage and selection operator (Lasso) for feature (variable) selection, termed a fuzzy Gaussian Lasso (FG-Lasso) clustering algorithm. We demonstrate that the proposed FG-Lasso is a good clustering algorithm with better choice for feature subset selection. Experimental results and comparisons actually present these good aspects of the proposed FG-Lasso clustering algorithm. Cancer is a disease with growth of abnormal cells in a body. WHO reported that it is the first or second main leading cause of death. It spreads and affects the other parts of body if there is not properly diagnosed. In the paper, we apply the proposed FG-Lasso to cancer data with good feature selection and clustering results.

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Keywords: fuzzy sets; model-based clustering; fuzzy model-based Gaussian; feature selection; Lasso; Fuzzy Gaussian Lasso (FG-Lasso) clustering

Citation: Miin-Shen Yang, Wajid Ali. Fuzzy Gaussian Lasso clustering with application to cancer data. Mathematical Biosciences and Engineering, 2020, 17(1): 250-265. doi: 10.3934/mbe.2020014

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