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 generalized association rules
Future Generation Computer Systems - Special double issue on data mining
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Granular support vector machines with association rules mining for protein homology prediction
Artificial Intelligence in Medicine
Mining changes in customer buying behavior for collaborative recommendations
Expert Systems with Applications: An International Journal
Aggregation of orders in distribution centers using data mining
Expert Systems with Applications: An International Journal
Mining changes in customer behavior in retail marketing
Expert Systems with Applications: An International Journal
Ranking discovered rules from data mining with multiple criteria by data envelopment analysis
Expert Systems with Applications: An International Journal
A Methodology for Exploring Association Models
Visual Data Mining
A temporal data mining approach for shelf-space allocation with consideration of product price
Expert Systems with Applications: An International Journal
| Hi-index | 12.07 |
In retailing, a variety of products compete to be displayed in the limited shelf space since it has a significant effect on demands. To affect customers' purchasing decisions, retailers properly make decisions about which products to display (product assortment) and how much shelf space to allocate the stocked products (shelf space allocation). In the previous studies, researchers usually employed the space elasticity to optimize product assortment and space allocation models. The space elasticity is usually used to construct the relationship between shelf space and product demand. However, the large number of parameters requiring to estimate and the he non-linear nature of space elasticity can reduce the efficacy of the space elasticity based models. This paper utilizes a popular data mining approach, association rule mining, instead of space elasticity to resolve the product assortment and allocation problems in retailing. In this paper, the multi-level association rule mining is applied to explore the relationships between products as well as between product categories. Because association rules are obtained by directly analyzing the transaction database, they can generate more reliable information to shelf space management.