In this paper, we handle a new kind of patterns named high on-shelf utility itemsets, which considers not only individual profit and quantity of each item in a transaction but also common on-shelf time periods of a product combination. We propose a three-scan mining approach to effectively and efficiently discover high on-shelf utility itemsets. The proposed approach adopts an itemset-generation mechanism to prune redundant candidates early and to systematically check the itemsets from transactions. The experimental results on synthetic datasets also show the proposed approach has a good performance.
|主出版物標題||Intelligent Information and Database Systems - Second International Conference, ACIIDS, Proceedings|
|出版狀態||Published - 17 九月 2010|
|事件||2010 Asian Conference on Intelligent Information and Database Systems, ACIIDS 2010 - Hue City, Viet Nam|
持續時間: 24 三月 2010 → 26 三月 2010
|名字||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||2010 Asian Conference on Intelligent Information and Database Systems, ACIIDS 2010|
|期間||24/03/10 → 26/03/10|