Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Discovering data mining: from concept to implementation
Discovering data mining: from concept to implementation
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Unexpectedness as a measure of interestingness in knowledge discovery
Decision Support Systems - Special issue on WITS '97
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
A Microeconomic View of Data Mining
Data Mining and Knowledge Discovery
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Methods and Problems in Data Mining
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Improving the Discovery of Association Rules with Intensity of Implication
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Applying Data Mining Techniques to a Health Insurance Information System
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A note on "beyond market baskets: generalizing association rules to correlations"
ACM SIGKDD Explorations Newsletter
Reducing redundancy in characteristic rule discovery by using integer programming techniques
Intelligent Data Analysis
Post-analysis of learned rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
High-utility pattern mining: A method for discovery of high-utility item sets
Pattern Recognition
A Methodology for Exploring Association Models
Visual Data Mining
Expert Systems with Applications: An International Journal
Why promotion strategies based on market basket analysis do not work
Expert Systems with Applications: An International Journal
Re-mining positive and negative association mining results
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Re-mining item associations: Methodology and a case study in apparel retailing
Decision Support Systems
Effective product assignment based on association rule mining in retail
Expert Systems with Applications: An International Journal
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It has been claimed that the discovery of association rules is well suited for applications of market basket analysis to reveal regularities in the purchase behaviour of customers. However today, one disadvantage of associations discovery is that there is no provision for taking into account the business value of an association. Therefore, recent work indicates that the discovery of interesting rules can in fact best be addressed within a microeconomic framework. This study integrates the discovery of frequent itemsets with a (microeconomic) model for product selection (PROFSET). The model enables the integration of both quantitative and qualitative (domain knowledge) criteria. Sales transaction data from a fully automated convenience store are used to demonstrate the effectiveness of the model against a heuristic for product selection based on product-specific profitability. We show that with the use of frequent itemsets we are able to identify the cross-sales potential of product items and use this information for better product selection. Furthermore, we demonstrate that the impact of product assortment decisions on overall assortment profitability can easily be evaluated by means of sensitivity analysis.