A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Efficiently Mining Maximal Frequent Itemsets
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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
SmartMiner: A Depth First Algorithm Guided by Tail Information for Mining Maximal Frequent Itemsets
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
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In a warehouse, if we can identify a small subset of items that can satisfy a large percentage of orders, we can improve the warehousing performance by assigning the small subset of items to a highly automated order completion zone. Current approach to identify such a small subset of items is heuristic based and does not guarantee a good solution. In this paper, we propose a novel approach to identify all the small subsets of items that can satisfy a large percentage of orders. We show that, with simple transformations, we can use the existing frequent itemset mining software or algorithm to solve the problem. Our approach has all the advantages provided by frequent itemset mining, including the capability of considering associations among items and the capability of handling a large order database. Our approach can be implemented with little effort by using existing frequent itemset mining software or algorithm. Furthermore, our approach guarantees all the desired solutions and allows the decision maker the fullest flexibility to plan the warehouse. Experiments on an order database of a real warehouse were performed to demonstrate the effectiveness of this approach. The experimental results show that our approach outperforms the existing approach.