Online mining of temporal maximal utility itemsets from data streams

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

37 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationAPPLIED COMPUTING 2010 - The 25th Annual ACM Symposium on Applied Computing
Pages1622-1626
Number of pages5
DOIs
StatePublished - 23 Jul 2010
Event25th Annual ACM Symposium on Applied Computing, SAC 2010 - Sierre, Switzerland
Duration: 22 Mar 201026 Mar 2010

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Conference

Conference25th Annual ACM Symposium on Applied Computing, SAC 2010
CountrySwitzerland
CitySierre
Period22/03/1026/03/10

Keywords

  • data stream mining
  • maximal itemsets
  • temporal high utility itemsets
  • utility mining

Fingerprint Dive into the research topics of 'Online mining of temporal maximal utility itemsets from data streams'. Together they form a unique fingerprint.

Cite this