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
An application of DEA to measure branch cross selling efficiency
Computers and Operations Research - Special issue on data envelopment analysis
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Building an Association Rules Framework to Improve Product Assortment Decisions
Data Mining and Knowledge Discovery
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Association rules mining in vertically partitioned databases
Data & Knowledge Engineering - Special issue: WIDM 2004
Efficient association rule mining among both frequent and infrequent items
Computers & Mathematics with Applications
An Efficient Algorithm for Mining Large Item Sets
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
A Frequent Item Graph Approach for Discovering Frequent Itemsets
ICACTE '08 Proceedings of the 2008 International Conference on Advanced Computer Theory and Engineering
Mining frequent itemsets in data streams using the weighted sliding window model
Expert Systems with Applications: An International Journal
Generalized association rule mining using an efficient data structure
Expert Systems with Applications: An International Journal
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Much academic research has been conducted about the process of association rule mining. More effort is now required for practical application of association rules in various commercial fields. A potential application of association rule mining is the problem of product assignment in retail. The product assignment problem involves how to most effectively assign items to sites in retail stores to grow sales. Effective product assignment facilitates cross-selling and convenient shopping for customers to promote maximum sales for retailers. However, little practical research has been done to address the issue. The current study approaches the product assignment problem using association rule mining for retail environments. There are some barriers to overcome in applying association rule mining to the product assignment problem for retail. This study conducts some generalizing to overcome drawbacks caused by the short lifecycles of current products. As a measure of cross-selling, lift is used to compare the effectiveness of various assignments for products. The proposed algorithm consists of three processes, which include mining associations among items, nearest neighbor assignments, and updating assignments. The algorithm was tested on synthetic databases. The results show very effective product assignment in terms of the potential for cross-selling to drive maximum sales for retailers.