Title :
Statistical scheme via AIC for evaluating the optimal cut off level in fuzzy clustering
Author :
Kanagawa, Shuya ; Shinkai, Kimiaki ; Chung, Hsunhsun ; Nagashima, Kenichi
Author_Institution :
Dept. Ind. & Manage. Syst., Tokyo City Univ., Tokyo, Japan
Abstract :
In this paper we show a new statistical scheme to find the optimal cut off level in fuzzy clustering which is an improvement of Uesu and Shinkai et. al [4]~[7]. Deterministic algorithms which seek a certain equilibrium cluster level have essential disadvantage in principle. We focus in it and propose a statistical scheme via AIC.
Keywords :
fuzzy set theory; pattern clustering; statistical analysis; AIC; deterministic algorithms; equilibrium cluster level; fuzzy clustering; optimal cut off level evaluation; statistical scheme; Clustering algorithms; Gradient methods; Histograms; Humans; Information theory; Partitioning algorithms; AIC; cut off level; fuzzy clustering; partition tree;
Conference_Titel :
Fuzzy Systems (FUZZ), 2011 IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-7315-1
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2011.6007559
