DocumentCode
349595
Title
Fuzzy clustering for uncertainty data
Author
Sato-Ilic, Mika
Author_Institution
Inst. of Policy & Planning Sci., Tsukuba Univ., Ibaraki, Japan
Volume
1
fYear
1999
fDate
1999
Firstpage
359
Abstract
This paper proposes a clustering model which can capture the change of vagueness included in data when the data is observed through several times and the vagueness is changed according to the times. In this paper, the vagueness is treated as fuzzy data, that is, it is defined as convex normal fuzzy sets. Due to the definitions of the different vagueness of each observation, the dissimilarity (or similarity) between a pair of objects has the property of asymmetric relation. This numerical example shows the validity of the model
Keywords
fuzzy set theory; pattern clustering; uncertainty handling; asymmetric relation; clustering model; convex normal fuzzy sets; fuzzy clustering; fuzzy data; object pair dissimilarity; uncertainty data; vagueness; Clustering algorithms; Clustering methods; Data analysis; Electronic mail; Frequency; Fuzzy sets; Industrial relations; Statistical analysis; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on
Conference_Location
Tokyo
ISSN
1062-922X
Print_ISBN
0-7803-5731-0
Type
conf
DOI
10.1109/ICSMC.1999.814117
Filename
814117
Link To Document