Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Global corporate web sites: an empirical investigation of content and design
Information and Management
Mining Frequent Itemsets Using Support Constraints
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Mining itemset utilities from transaction databases
Data & Knowledge Engineering - Special issue: ER 2003
Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective
Information Systems Research
High-utility pattern mining: A method for discovery of high-utility item sets
Pattern Recognition
Journal of Management Information Systems
Mining high utility itemsets in large high dimensional data
Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop
Expert Systems with Applications: An International Journal
Generalized association rule mining using an efficient data structure
Expert Systems with Applications: An International Journal
An improved association rules mining method
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Recently, a utility-based mining approach has emerged as an alternative mechanism to frequency-based mining in an attempt to reflect not only the statistical correlation but also the semantic significance (e.g., price and quantity) of items. However, existing mining trajectories utilizing high-utility itemsets may not offer firms sufficient business insights unless they can precisely assess the value of association rules, which may vary substantially depending on many business parameters included in the assessment. In this study, we propose a utility-based association-rule mining method that valuates association rules by measuring their specific business benefits accruing to firms. Based on previous studies, three key elements (opportunity, effectiveness, and probability) are identified to define and operationalize a users' preference as a utility function. To apply the utility-based mechanism to the processing of large transaction databases, we constructed functional algorithms, with heightened attention paid to their pruning strategies, and evaluated them based on real-world databases. Experimental results show that the proposed approach can provide users with greater business benefits than the high-utility itemset mining approach, suggesting several important strategic implications for both research and practice.