DocumentCode
145650
Title
Type-2 Context-Based FCM Clustering and Its Model
Author
Sung-Suk Kim ; Keun-Chang Kwak
Author_Institution
Dept. of Control & Instrum. Eng., Chosun Univ., Gwangju, South Korea
Volume
2
fYear
2014
fDate
10-13 March 2014
Firstpage
309
Lastpage
310
Abstract
In this paper, we propose a Type-2 Context-based Fuzzy C-Means (T2-CFCM) clustering algorithm and its linguistic model. This clustering technique builds information granules in the form of Type-2 fuzzy sets and develops clusters by preserving the homogeneity of the clustered patterns associated with the input and output space. The fundamental idea of conditional fuzzy clustering and Linguistic Model (LM) introduced by Pedrycz. Finally, we present the architecture and reasoning scheme of LM based on T2-CFCM clustering.
Keywords
fuzzy set theory; linguistics; pattern clustering; T2-CFCM clustering algorithm; architecture scheme; conditional fuzzy clustering; information granules; linguistic model; reasoning scheme; type-2 context-based FCM clustering; type-2 fuzzy sets; Clustering algorithms; Clustering methods; Context; Context modeling; Fuzzy sets; Pragmatics; Probabilistic logic; Type-2 fuzzy sets; context-based fuzzy c-means; information granules; linguistic model;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Science and Computational Intelligence (CSCI), 2014 International Conference on
Conference_Location
Las Vegas, NV
Type
conf
DOI
10.1109/CSCI.2014.148
Filename
6822359
Link To Document