Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
An approach to mining bundled commodities
Knowledge-Based Systems
Kansei evaluation based on prioritized multi-attribute fuzzy target-oriented decision analysis
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A Hybrid Kansei Design Expert System Using Artificial Intelligence
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Expert Systems with Applications: An International Journal
Symbiosis: creativity with affective response
EPCE'07 Proceedings of the 7th international conference on Engineering psychology and cognitive ergonomics
Expert Systems with Applications: An International Journal
Feature fatigue analysis in product development using Bayesian networks
Expert Systems with Applications: An International Journal
A tunable swarm-optimization-based approach for affective product design
MACMESE'07 Proceedings of the 9th WSEAS international conference on Mathematical and computational methods in science and engineering
A multi-objective genetic algorithm approach to rule mining for affective product design
Expert Systems with Applications: An International Journal
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
Kansei games: entertaining emotions
ICEC'07 Proceedings of the 6th international conference on Entertainment Computing
Design by customer: concept and applications
Journal of Intelligent Manufacturing
Advanced Engineering Informatics
A study on multiattribute aggregation approaches to product recommendation
Advances in Fuzzy Systems
Affective and cognitive design for mass personalization: status and prospect
Journal of Intelligent Manufacturing
Hi-index | 12.06 |
Affective design has received much attention from both academia and industries. It aims at incorporating customers' affective needs into design elements that deliver customers' affective satisfaction. The main challenge for affective design originates from difficulties in mapping customers' subjective impressions, namely Kansei, to perceptual design elements. This paper intends to develop an explicit decision support to improve the Kansei mapping process by reusing knowledge from past sales records and product specifications. As one of the important applications of data mining, association rule mining lends itself to the discovery of useful patterns associated with the mapping of affective needs. A Kansei mining system is developed to utilize valuable affect information latent in customers' impressions of existing affective designs. The goodness of association rules is evaluated according to their achievements of customers' expectations. Conjoint analysis is applied to measure the expected and achieved utilities of a Kansei mapping relationship. Based on goodness evaluation, mapping rules are further refined to empower the system with useful inference patterns. The system architecture and implementation issues are discussed in detail. An application of Kansei mining to mobile phone affective design is presented.