Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Unexpectedness as a measure of interestingness in knowledge discovery
Decision Support Systems - Special issue on WITS '97
Methodological and practical aspects of data mining
Information and Management
Data mining for customer service support
Information and Management
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Mining Optimized Association Rules with Categorical and Numeric Attributes
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Integrating AHP and data mining for product recommendation based on customer lifetime value
Information and Management
Safely delegating data mining tasks
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Searching customer patterns of mobile service using clustering and quantitative association rule
Expert Systems with Applications: An International Journal
Mining association rules from imprecise ordinal data
Fuzzy Sets and Systems
A novel approach for discovering retail knowledge with price information from transaction databases
Expert Systems with Applications: An International Journal
Context-based market basket analysis in a multiple-store environment
Decision Support Systems
Protecting business intelligence and customer privacy while outsourcing data mining tasks
Knowledge and Information Systems
Multi-level association rules for MP3P marketing strategies based on extensive marketing survey data
Expert Systems with Applications: An International Journal
Electronic Commerce Research and Applications
Mining the change of customer behavior in fuzzy time-interval sequential patterns
Applied Soft Computing
Electronic Commerce Research and Applications
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This paper applies data mining techniques to extract retailing knowledge from the POS information provided by an inter-organizational information service center in Taiwan. Many mutually competitive retail chains sponsored the data warehouse. They must, of course, protect their secrets, while cooperating to mine the inter-organizational data and thereby extract macro-level knowledge about consumers' behavior. Many difficulties arise from this, because each transaction contains only a summary indicating the total sales of a single product in a store during a month and more detailed data are not available. Moreover, with many retail store chains cooperating, the meaning of the quantitative data, such as price and quantity, is difficult to compare and hard to interpret. No previous research addressed this problem. A series of steps were implemented to help solve this problem; they include defining semantic association rules (AR), transforming the quantitative data into semantic data and developing algorithms for mining the knowledge. Finally, we consolidated these ideas and implemented a prototype system.