한국감성과학회:학술대회논문집 (Proceedings of the Korean Society for Emotion and Sensibility Conference)
- 한국감성과학회 2002년도 춘계학술대회 논문집
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- Pages.195-200
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- 2002
HIERARCHICAL CLUSTER ANALYSIS by arboART NEURAL NETWORKS and its APPLICATION to KANSEI EVALUATION DATA ANALYSIS
- Ishihara, Shigekazu (Department of Kansei Ergonomics, College of Human and Social Environmental, Hiroshima International University) ;
- Ishihara, Keiko (Department of Language and Communication, College of Human and Social Environment, Hiroshima International University) ;
- Nagamachi, Mitsuo (Department of Kansei Ergonomics, College of Human and Social Environment, Hiroshima International University)
- 발행 : 2002.05.01
초록
ART (Adaptive Resonance Theory [1]) neural network and its variations perform non-hierarchical clustering by unsupervised learning. We propose a scheme "arboART" for hierarchical clustering by using several ART1.5-SSS networks. It classifies multidimensional vectors as a cluster tree, and finds features of clusters. The Basic idea of arboART is to use the prototype formed in an ART network as an input to other ART network that has looser distance criteria (Ishihara, et al., [2,3]). By sending prototype vectors made by ART to one after another, many small categories are combined into larger and more generalized categories. We can draw a dendrogram using classification records of sample and categories. We have confirmed its ability using standard test data commonly used in pattern recognition community. The clustering result is better than traditional computing methods, on separation of outliers, smaller error (diameter) of clusters and causes no chaining. This methodology is applied to Kansei evaluation experiment data analysis.
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