A grey-based rough approximation model for interval data processing
Information Sciences: an International Journal
Information Filter for Ambiguous Information Retrieval
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Search in the mood: the information filter based on ambiguous queries
International Journal of Computer Applications in Technology
Employing rough sets and association rule mining in KANSEI knowledge extraction
Information Sciences: an International Journal
ANFIS modeling for predicting affective responses to tactile textures
Human Factors in Ergonomics & Manufacturing
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M. Nagamachi founded Kansei Engineering at Hiroshima University about 30 years ago and it has spread out in the world as an ergonomic consumer-oriented product development. The aim of the kansei engineering is to develop a new product by translating a customer’s psychological needs and feeling (kansei) concerning it into design specifications. The kansei data are analyzed by a multivariate statistical analysis to create the new products so far, but the kansei data not always have linear features assumed under the normal distribution. Rough sets theory is able to deal with any kind of data, irrespective of linear or non-linear characteristics of the data. We compare the results based on statistical analysis and on Rough Sets Theory.