DocumentCode :
3117699
Title :
FCMdd-type linear fuzzy clustering for incomplete non-Euclidean relational data
Author :
Yamamoto, Takeshi ; Honda, Katsuhiro ; Notsu, Akira ; Ichihashi, Hidetomo
Author_Institution :
Dept. of Comput. Sci. & Intell. Syst., Osaka Prefecture Univ., Sakai, Japan
fYear :
2011
fDate :
27-30 June 2011
Firstpage :
792
Lastpage :
798
Abstract :
A linear fuzzy clustering model based on Fuzzy c-Medoids (FCMdd) was proposed for extracting intrinsic local linear substructures from relational data. In the same way with Non-Euclidean Relational Fuzzy (NERF) c-Means, β-spread transformation was also proved to be useful for handling non Euclidean relational data in FCMdd-type linear clustering. In this paper, the FCMdd-type linear clustering model is further modified in order to handle incomplete data including missing values. In several numerical experiments, it is demonstrated that some pre-imputation strategies contribute to properly selecting representative medoids of each cluster.
Keywords :
data handling; fuzzy set theory; pattern clustering; relational databases; FCMdd-type linear fuzzy clustering model; NERF c-means clustering; beta-spread transformation; fuzzy c-medoids; incomplete nonEuclidean relational data handling; intrinsic local linear substructure extraction; nonEuclidean relational fuzzy c-means clustering; Clustering algorithms; Data mining; Data models; Eigenvalues and eigenfunctions; Euclidean distance; Image color analysis; Prototypes; linear fuzzy clustering; missing value; relational clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems (FUZZ), 2011 IEEE International Conference on
Conference_Location :
Taipei
ISSN :
1098-7584
Print_ISBN :
978-1-4244-7315-1
Electronic_ISBN :
1098-7584
Type :
conf
DOI :
10.1109/FUZZY.2011.6007379
Filename :
6007379
Link To Document :
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