Granular Computing Based on Gaussian Cloud Transformation

• Autorzy: Liu Y. , Li D. , He W. , Wang G.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

data clustering; Gaussian cloud model; GCM; granular computing; image segmentation

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2013
• Tom: Vol. 127, nr 1-4
• Strony: 385--398
• Opis fizyczny: Bibliogr. 33 poz., rys., tab., wykr.

Twórcy

autor

Liu Y.

• Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

autor

Li D.

• Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

autor

He W.

• Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

autor

Wang G.

• Institute of Electronic Information Technology, Chongqing Institute of Green and Intelligent Technology, CAS, Chongqing 401122, China

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