DocumentCode :
3269210
Title :
Robust FCMdd-based Linear Clustering for Relational Data with Alternative c-Means Criterion
Author :
Yamamoto, Takeshi ; Honda, Katsuhiro ; Notsu, Akira ; Ichihashi, Hidetomo
Author_Institution :
Dept. of Comput. Sci. & Intell. Syst., Osaka Prefecture Univ., Sakai, Japan
Volume :
2
fYear :
2011
fDate :
18-21 Dec. 2011
Firstpage :
334
Lastpage :
337
Abstract :
Relational clustering is actively studied in data mining, in which intrinsic data structure is summarized into cluster structure. A linear fuzzy clustering model based on Fuzzy c-Medoids (FCMdd) is proposed for extracting intrinsic local linear substructures from relational data. Alternative Fuzzy c-Means (AFCM) is an extension of Fuzzy c-means, in which a modified distance measure instead of the conventional Euclidean distance is used based on the robust M-estimation concept. In this paper, the FCMdd-based linear clustering model is further modified in order to extract linear substructure from relational data including outliers, using a pseudo-M-estimation procedure with a weight function for the modified distance measure in AFCM.
Keywords :
data mining; fuzzy set theory; pattern clustering; relational databases; Euclidean distance; Fuzzy c-means; alternative c-means criterion; data mining; fuzzy c-Medoids; intrinsic data structure; linear fuzzy clustering model; relational clustering; relational data; robust FCMdd based linear clustering; weight function; Data mining; Data models; Euclidean distance; Noise; Noise measurement; Prototypes; Robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications and Workshops (ICMLA), 2011 10th International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
978-1-4577-2134-2
Type :
conf
DOI :
10.1109/ICMLA.2011.164
Filename :
6147699
Link To Document :
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