Granular Computing Based on Gaussian Cloud Transformation

  • Authors:

  • Yuchao Liu;Deyi Li;Wen He;Guoyin Wang

  • Affiliations:

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

  • Venue:
  • Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
  • Year:
  • 2013

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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.