Open AccessFuzzy Gaussian Lasso clustering with application to cancer data
Author(s) -
Miin-Shen Yang,
Wajid Ali
Publication year - 2020
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2020014
Subject(s) - lasso (programming language) , cluster analysis , feature selection , fuzzy clustering , fuzzy logic , pattern recognition (psychology) , artificial intelligence , correlation clustering , gaussian , mathematics , computer science , feature (linguistics) , clustering high dimensional data , data mining , algorithm , physics , quantum mechanics , world wide web , linguistics , philosophy
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.