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  2018, Vol. 31 Issue (8): 677-692    DOI: 10.16451/j.cnki.issn1003-6059.201808001
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Similarity Measure of Multi-granularity Cloud Model
YANG Jie1,2, WANG Guoyin1, ZHANG Qinghua1, FENG Lin3
1.Chongqing Key Laboratory of Computational Intelligence, Ch-ongqing University of Post and Telecommunications, Chong-qing 400065
2.School of Physics and Electronic Science, Zunyi Normal University, Zunyi 563002
3.School of Computer Science, Sichuan Normal University, Chengdu 610101

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Abstract  

Traditional cloud similarity measures are only suitable for single granularity, and the multi-granularity cloud similarity measure is insufficient in research. In this paper, a knowledge distance framework is proposed and its relative properties are proved. The relationships between knowledge distance and information measure and information granularity are established. Moreover, two valuable conclusions are drawn in a hierarchical granular structure. The granularity difference of two granular spaces in a hierarchical granular structure is positive correlation to the knowledge distance framework between them. The granular spaces change continuously with the granularity and they can be mapped into the one-dimension coordinate. Finally, a similarity measure of cloud model based on the knowledge distance framework(KDFCM) is proposed. The experiments verify that the properties of KDFCM are consistent with the above conclusions.

Key wordsCloud Model      Knowledge Distance      Hierarchical Granular Structure      Similarity Measure     
Received: 15 April 2018     
ZTFLH: TP 311  
Fund:

Supported by National Natural Science Foundation of China(No.61572091,61472056), High-End Talent Project(No.RC20160
05), Qian Education Cooperation(No.LH (2017) No.7075), Sichuan National Science and Technology Support Program(No.2015GZ0079), Key Disciplines of Guizhou Province (No.QXWB[2013]18)

Corresponding Authors: WANG Guoyin, Ph.D. professor. His research interests include rough set, granular computing, intelligent information processing and data mining.   
About author:: YANG Jie, Ph.D. candidate, associate professor. His research interests include cloud model, granular computing, rough set and machine learning. ZHANG Qinghua, Ph.D., professor. His research interests include rough set, fuzzy set, granular computing and three-way decisions. FENG Lin, Ph.D., professor. His research interests include rough set, granular computing and machine learning.
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YANG Jie
WANG Guoyin
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FENG Lin
Cite this article:   
YANG Jie,WANG Guoyin,ZHANG Qinghua等. Similarity Measure of Multi-granularity Cloud Model[J]. , 2018, 31(8): 677-692.
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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201808001      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I8/677
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