Data mining: concepts and techniques
Data mining: concepts and techniques
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
CCAIIA: Clustering Categorial Attributed into Interseting Accociation Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Quality Function Deployment (QFD): an effective technique for requirements acquisition and reuse
ISESS '95 Proceedings of the 2nd IEEE Software Engineering Standards Symposium
A survey of evolutionary algorithms for data mining and knowledge discovery
Advances in evolutionary computing
Appropriateness and Impact of Platform-Based Product Development
Management Science
Multi-objective rule mining using genetic algorithms
Information Sciences: an International Journal - Special issue: Soft computing data mining
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Simulation-based conjoint ranking for optimal decision support process under aleatory uncertainty
Journal of Intelligent Manufacturing
Mass customization in the product life cycle
Journal of Intelligent Manufacturing
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In the initial stage of product design, it is essential to define product specifications according to various market niches. An important issue in this process is to provide designers with sufficient design knowledge to find out what customers really want. This paper proposes a data mining method to facilitate this task. The method focuses on mining association rules that reflect the mapping relationship between customer needs and product specifications. Four objectives, support, confidence, interestingness and comprehensibility, are used for evaluating the extracted rules. To solve such a multi-objective problem, a Pareto-based GA is utilized to perform the rule extraction. Through computational experiments on an electrical bicycle case, it is shown that our approach is capable of extracting useful and interesting knowledge from a design database.