Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data mining
Using modeling knowledge to guide design space search
Artificial Intelligence
Automated capture of rationale for the detailed design process
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Data Mining to Predict Aircraft Component Replacement
IEEE Intelligent Systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
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Effective collection and analysis of customer demand is a critical success factor for new product design and development. This chapter presents a set of customer requirement discovery methodologies to achieve broad and complex market studies for new products. The proposed approach uses data mining and text mining technologies to discover customer multi-preference and corresponding customer motivation. Using the proposed rule mining methodology, discovery rules can be flexibly defined, the complete customer multi-preference patterns are discovered and their statistic analysis of multi-preference can be conducted for new product design. With the proposed text mining methodology, the customer motivations are discovered and the percentage of surveyed customers with certain preference and the reason for this preference are presented. Combining the methodologies in text mining with rule mining, the customer motivations can be quantitatively described with statistic analysis results. A prototype system that allows on-line customer feedback collection, digitization of the language feedbacks, numerical descriptions of customer preferences and customer motivation of a product is developed to demonstrate the feasibility of the proposed methodologies. It is shown that the proposed work could significantly shorten the survey and analysis time for customer preference and is thus expected to help companies to reduce cycle time for new product design.