Title :
arboART: ART based hierarchical clustering and its application to questionnaire data analysis
Author :
Ishihara, Shigekazu ; Ishihara, Keiko ; Nagamachi, Mitsuo ; Matsubara, Yukihiro
Author_Institution :
Onomichi Junior Coll., Japan
Abstract :
A hierarchical clustering mechanism is designed to analyze multidimensional data and feature selection based on ART-type neural networks. Prototype of clusters obtained from an ART´s top-down vectors are sent to another ART. Several ART networks that have different similarity criteria are used for cluster combination. This scheme of hierarchical clustering (arboART) enables to make a tree structure graph of classification result of samples, and find features of each cluster. arboART is utilized to automatic rule generation of Kansei engineering expert systems. Analyzing result on color evaluation experiment by arboART and comparison with conventional multivariate analysis are shown
Keywords :
ART neural nets; data analysis; pattern recognition; trees (mathematics); ART-based hierarchical clustering; Kansei engineering expert systems; arboART; automatic rule generation; classification; feature selection; hierarchical clustering mechanism; multidimensional data; questionnaire data analysis; top-down vectors; tree structure graph; Art; Classification tree analysis; Data analysis; Expert systems; Multidimensional systems; Neural networks; Prototypes; Subspace constraints; Systems engineering and theory; Tree data structures;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.488234
