DocumentCode :
2620202
Title :
DCf: a double clustering framework for fuzzy information granulation
Author :
Castellano, Giovanna ; Fanelli, Anna M. ; Mencar, Corrado
Author_Institution :
Dept. of Informatics, Bari Univ., Italy
Volume :
2
fYear :
2005
fDate :
25-27 July 2005
Firstpage :
397
Abstract :
In this paper, we present a framework for extracting well-defined and semantically sound information granules. The framework is mainly centered on a double clustering process, hence, it is called DCf (double clustering framework). A first clustering process identifies cluster prototypes in the multidimensional data space, then the projections of these prototypes are further clustered along each dimension to provide a granulation of data. Finally, the extracted granules are described in terms of fuzzy sets that meet interpretability constraints so as to provide a qualitative description of the information granules. Different implementations of DCf are presented and compared on a medical diagnosis problem to show the utility of the proposed framework.
Keywords :
fuzzy set theory; patient diagnosis; pattern clustering; data granulation; double clustering framework; fuzzy information granulation; fuzzy set; information extraction; information granule; interpretability constraint; medical diagnosis; Clustering algorithms; Data mining; Failure analysis; Fuzzy sets; Informatics; Information analysis; Information processing; Medical diagnosis; Multidimensional systems; Prototypes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Granular Computing, 2005 IEEE International Conference on
Print_ISBN :
0-7803-9017-2
Type :
conf
DOI :
10.1109/GRC.2005.1547320
Filename :
1547320
Link To Document :
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