Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
How Web Site Decision Technology Affects Consumers
IEEE Internet Computing
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Helping Online Customers Decide through Web Personalization
IEEE Intelligent Systems
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Web Structure Mining for Usability Analysis
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Visual Interface for Online Watching of Frequent Itemset Generation in Apriori and Eclat
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
ACM Computing Surveys (CSUR)
Consumer Modelling in Support of Interface Design
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 02
The rough set exploration system
Transactions on Rough Sets III
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Consumer research has indicated that consumers use compensatory and non-compensatory decision strategies when formulating their purchasing decisions. Compensatory decision-making strategies are used when the consumer fully rationalizes their decision outcome whereas non-compensatory decision-making strategies are used when the consumer considers only that information which has most meaning to them at the time of decision. When designing online shopping support tools, incorporating these decision-making strategies with the goal of personalizing the design of the user interface may enhance the overall quality and satisfaction of the consumer's shopping experiences. This paper presents work towards this goal. The authors describe research that refines a previously developed procedure, using techniques in cluster analysis and rough sets, to obtain consumer information needed in support of designing customizable and personalized user interface enhancements. The authors further refine their procedure by examining and evaluating techniques in traditional association mining, specifically conducting experimentation using the Eclat algorithm for use with the authors' previous work. A summary discussing previous work in relation to the new evaluation is provided. Results are analyzed and opportunities for future work are described.