Computational geometry: an introduction
Computational geometry: an introduction
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)
Knowledge management and data mining for marketing
Decision Support Systems - Knowledge management support of decision making
On the Complexity of Mining Quantitative Association Rules
Data Mining and Knowledge Discovery
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
On Mining General Temporal Association Rules in a Publication Database
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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 frequent item sets by opportunistic projection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Context-based market basket analysis in a multiple-store environment
Decision Support Systems
Blind paraunitary equalization
Signal Processing
A change detection method for sequential patterns
Decision Support Systems
Global data mining: An empirical study of current trends, future forecasts and technology diffusions
Expert Systems with Applications: An International Journal
Utility-based association rule mining: A marketing solution for cross-selling
Expert Systems with Applications: An International Journal
International Journal of Business Intelligence and Data Mining
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Recent marketing research has suggested that in-store environmental stimuli, such as shelf-space allocation and product display, has a great influence upon consumer buying behavior and may induce substantial demand. Prior work in this area, however, has not considered the effect of spatial relationships, such as the shelf-space adjacencies of distinct items, on unit sales. This paper, motivated in great part by the prominent beer and diapers example, uses data mining techniques to discover the implicit, yet meaningful, relationship between the relative spatial distance of displayed products and the items' unit sales in a retailer's store. The purpose of the developed mining scheme is to identify and classify the effects of such relationships. The managerial implications of the discovered knowledge are crucial to the retailer's strategic formation in merchandising goods. This paper proposes a novel representation scheme and develops a robust algorithm based on association analysis. To show its efficiency and effectiveness, an intensive experimental study using self-defined simulation data was conducted. The authors believe that this is the first academically researched attempt at exploring this emerging area of the merchandising problem using data mining.