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Granular Computing Based on Gaussian Cloud Transformation
Liu, Yuchao1; Li, Deyi1; He, Wen1; Wang, Guoyin2
2013
摘要Granular computing is one of the important methods for extracting knowledge from data and has got great achievements. However, it is still a puzzle for granular computing researchers to imitate the human cognition process of choosing reasonable granularities automatically for dealing with difficult problems. In this paper, a Gaussian cloud transformation method is proposed to solve this problem, which is based on Gaussian Mixture Model and Gaussian Cloud Model. Gaussian Mixture Model (GMM) is used to transfer an original data set to a sum of Gaussian distributions, and Gaussian Cloud Model (GCM) is used to represent the extension of a concept and measure its confusion degree. Extensive experiments on data clustering and image segmentation have been done to evaluate this method and the results show its performance and validity.
关键词Granular computing Gaussian Mixture Model Gaussian Cloud Model Data clustering Image segmentation
DOI10.3233/FI-2013-916
发表期刊FUNDAMENTA INFORMATICAE
ISSN0169-2968
卷号127期号:1-4页码:385-398
通讯作者Wang, GY (reprint author), Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Inst Elect Informat Technol, Chongqing 401122, Peoples R China.
收录类别SCI
WOS记录号WOS:000325745600028
语种英语