Title :
Fuzzy clustering for uncertainty data
Author_Institution :
Inst. of Policy & Planning Sci., Tsukuba Univ., Ibaraki, Japan
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;
Conference_Titel :
Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on
Conference_Location :
Tokyo
Print_ISBN :
0-7803-5731-0
DOI :
10.1109/ICSMC.1999.814117
