Affiliations: Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China | Institute of Electronic Information Technology, Chongqing Institute of Green and Intelligent Technology, CAS, Chongqing 401122, China. wanggy@ieee.org
Note: [] This work is supported by Chongqing Scientific and Technological Program under Grant No. CSTC2013JJB40003 and the Key Program of the National Natural Science Foundation of China under Grant No. 61035004, 61272060 and 91120306.
Note: [] Address for correspondence: Institute of Electronic Information Technology, Chongqing Institute of Green and Intelligent Technology, CAS, Chongqing 401122, China
Abstract: 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.
