Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Affective computing
Data mining: concepts and techniques
Data mining: concepts and techniques
A Hierarchical Model to Support Kansei Mining Process
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Kansei evaluation based on prioritized multi-attribute fuzzy target-oriented decision analysis
Information Sciences: an International Journal
A dominance-based rough set approach to Kansei Engineering in product development
Expert Systems with Applications: An International Journal
A Kansei mining system for affective design
Expert Systems with Applications: An International Journal
Associating visual textures with human perceptions using genetic algorithms
Information Sciences: an International Journal
An interactive evolutionary design system with feature extraction
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: applications and services
Kansei engineering and rough sets model
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Variable precision bayesian rough set model and its application to human evaluation data
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Information-theoretic measures associated with rough set approximations
Information Sciences: an International Journal
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KANSEI Engineering (KE) is a method for translating feelings and impressions into product parameters and the objective of KANSEI Engineering is to study the relationship between product forms and KANSEI images. It is most important to extract critical form features of the product relative to specific KANSEI adjectives through a WEB-based KANSEI information system. In this paper, critical form features and KANSEI adjectives were defined as condition attributes and decision attributes respectively, which were formalized as two objects in Decision Table (DT). Then, the Semantic Differential (SD), which measures the connotative meaning of concepts, was applied to evaluate form features of the product through a KANSEI questionnaire system. The evaluation record from an individual's transaction data was reserved if its frequency was higher than the given threshold. Some form features were deleted by using an attribute reduction algorithm based on Rough Sets Theory (RST). Furthermore, the size of the DT was reduced by using a rule-joining operation. A strong association rule set which describes the relationship between the critical form features and the corresponding KANSEI adjectives was subsequently generated. A case study of a mobile phone design was presented to demonstrate the effectiveness of the proposed method by comparing it with other non-linear data mining methods in KANSEI Engineering.