DocumentCode
1661383
Title
Linear fuzzy clustering based on least absolute deviations
Author
Honda, Katsuhiro ; Togo, Nobuhiro ; Fujii, Taro ; Ichihashi, Hidetomo
Author_Institution
Graduate Sch. of Eng., Osaka Prefecture Univ., Japan
Volume
2
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
1444
Lastpage
1449
Abstract
This paper proposes a technique of linear fuzzy clustering based on least absolute deviations. The novel method partitions a data set into several linear clusters by extracting local minor components. Using the least absolute deviations, the method provides robust clustering that is free from the influences of outliers
Keywords
fuzzy set theory; pattern clustering; data set partitioning; least absolute deviations; linear fuzzy clustering; local minor component extraction; outliers; robust clustering; Clustering algorithms; Clustering methods; Data mining; Eigenvalues and eigenfunctions; Fuzzy sets; Principal component analysis; Prototypes; Robustness; Scattering; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on
Conference_Location
Honolulu, HI
Print_ISBN
0-7803-7280-8
Type
conf
DOI
10.1109/FUZZ.2002.1006717
Filename
1006717
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