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- 저자 : Shigekazu Ishihara (검색결과 2 건)
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( Shigekazu Ishihara ),( Keiko Ishihara ),( Mitsuo Nagamachi ) 한국감성과학회 2002 춘계학술대회 Vol.2002 No.-
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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Morphometrics for Shape Analysis in Kansei Engineering
Shigekazu Ishihara,Keiko Ishihara 대한전자공학회 2008 ITC-CSCC :International Technical Conference on Ci Vol.2008 No.7
In Kansei engineering, we have been treated sample shapes as categorical variable (nominal scale). For example, categories like wide / tall. These categories were assigned as x variables and evaluation values on a Kansei word was assigned as a y variable of linear equation. This equation has been computationally solved by Quantification theory type 1 or similar regression methods. Although qualitative analysis of shapes is relatively robust and commonly used, but shapes are not directly treated. In this study, we attempted to treat shapes as statistical values with the various methods of Morphometrics those have been developed between paleontology, biology and statistics. By treating shapes as statistical values, we can apply various statistical methods from basic statistics such as testing distribution to multivariate analysis techniques (i.e., classification, projection onto lower numbers of dimension).
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