Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 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 fuzzy association rules in databases
ACM SIGMOD Record
Mining the most interesting rules
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
An approach to discovering temporal association rules
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Mining multiple-level spatial association rules for objects with a broad boundary
Data & Knowledge Engineering
ACM Transactions on Information Systems (TOIS)
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
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
IEEE Transactions on Knowledge and Data Engineering
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Mining inter-organizational retailing knowledge for an alliance formed by competitive firms
Information and Management
Discovering Calendar-Based Temporal Association Rules
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
Algorithms for mining association rules in bag databases
Information Sciences—Informatics and Computer Science: An International Journal
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Market basket analysis in a multiple store environment
Decision Support Systems
A sampling-based method for mining frequent patterns from databases
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Discovering fuzzy time-interval sequential patterns in sequence databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Decision analysis of data mining project based on Bayesian risk
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
With the advances in information technology and the emergence of Internet commerce, analysis of transaction data has become a crucial technique for effective decision-making and strategy formation in business operations. It is especially critical for retail management, in both online and brick-and-mortar stores. Traditional research in mining retail knowledge, however, does not take into account the products' prices and how such settings can affect potential demand. This paper opens a new research dimension by treating products' prices as an important decision variable in mining retail knowledge. To the best of our knowledge, the problem addressed in this paper has never been dealt with in existing research papers. We propose a representation scheme to incorporate price information into historical transaction data. An efficient algorithm is developed to ''dig'' out implicit, yet meaningful, patterns with price information. In addition, an extensive and well-designed experiment is executed, showing that the algorithm is computationally efficient and that the proposed analysis is significant and useful.