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This paper proposes a Kansei modeling technique by using the Rough-sets Theory based on a set of evaluation data for Kutani-ware coffee cups. Kutani-ware is a famous traditional craft that is a very important traditional industry in Japan. However, it has been shrinking recently because of the changes in lifestyle or the appearance of more functional modern products. To reactivate this industry by developing and recommending products that attract people’s feelings, this study develops a modeling technique for identifying relations between design and feeling by obtaining some if-and-then rules. An important contribution of the paper is that the proposed technique can suggest new designs by analyzing customers’ Kansei requirements, which are not used in the evaluation experiment. This makes the recommendation successful by determining people’s Kansei into data instead of attempts.

the aesthetic aspect plays a crucial role in human choice, thus studies dealing with subjective (called kansei in Japanese) evaluation and decision support by focusing on customers' psychological needs and personal taste became very essential. The purpose of the study is to illustrate two similar aggregation methods for multi-criteria analysis of a problem characterized by attributes of heterogeneous types which could be numerical, categorical, or descriptive, respectively. In this specific issue of supporting choice from a given set of traditional crafts in Japan, we are dealing with kansei attributes and kansei data (values of attributes) rather than the normal ones. Our primary purpose is to develop a web-based interactive system to support salesman in a shop or to support internet based selling by recognizing and proposing alternatives which fit the preferences of customers.

This paper proposes a Kansei modeling technique by using the Rough-sets Theory based on a set of evaluation data for Kutani-ware coffee cups. Kutani-ware is a famous traditional craft that is a very important traditional industry in Japan. However, it has been shrinking recently because of the changes in lifestyle or the appearance of more functional modern products. To reactivate this industry by developing and recommending products that attract people’s feelings, this study develops a modeling technique for identifying relations between design and feeling by obtaining some if-and-then rules. An important contribution of the paper is that the proposed technique can suggest new designs by analyzing customers’ Kansei requirements, which are not used in the evaluation experiment. This makes the recommendation successful by determining people’s Kansei into data instead of attempts.