Online mining of temporal maximal utility itemsets from data streams

Bai En Shie*, S. Tseng, Philip S. Yu

*Corresponding author for this work

研究成果: Conference contribution同行評審

39 引文 斯高帕斯(Scopus)

摘要

Data stream mining has become an emerging research topic in the data mining field, and finding frequent itemsets is an important task in data stream mining with wide applications. Recently, utility mining is receiving extensive attentions with two issues reconsidered: First, the utility (e.g., profit) of each item may be different in real applications; second, the frequent itemsets might not produce the highest utility. In this paper, we propose a novel algorithm named GUIDE (Generation of temporal maximal Utility Itemsets from Data strEams) which can find temporal maximal utility itemsets from data streams. A novel data structure, namely, TMUI-tree (Temporal Maximal Utility Itemset tree), is also proposed for efficiently capturing the utility of each itemset with one-time scanning. The main contributions of this paper are as follows: 1) GUIDE is the first one-pass utility-based algorithm for mining temporal maximal utility itemsets from data streams, and 2) TMUI-tree is efficient and easy to maintain. The experimental results show that our approach outperforms other existing utility mining algorithms like Two-Phase algorithm under the data stream environments.

原文English
主出版物標題APPLIED COMPUTING 2010 - The 25th Annual ACM Symposium on Applied Computing
頁面1622-1626
頁數5
DOIs
出版狀態Published - 23 七月 2010
事件25th Annual ACM Symposium on Applied Computing, SAC 2010 - Sierre, Switzerland
持續時間: 22 三月 201026 三月 2010

出版系列

名字Proceedings of the ACM Symposium on Applied Computing

Conference

Conference25th Annual ACM Symposium on Applied Computing, SAC 2010
國家Switzerland
城市Sierre
期間22/03/1026/03/10

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