An efficient algorithm for mining temporal high utility itemsets from data streams

Chun Jung Chu, S. Tseng*, Tyne Liang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

62 Scopus citations

Abstract

Utility of an itemset is considered as the value of this itemset, and utility mining aims at identifying the itemsets with high utilities. The temporal high utility itemsets are the itemsets whose support is larger than a pre-specified threshold in current time window of the data stream. Discovery of temporal high utility itemsets is an important process for mining interesting patterns like association rules from data streams. In this paper, we propose a novel method, namely THUI (Temporal High Utility Itemsets)-Mine, for mining temporal high utility itemsets from data streams efficiently and effectively. To the best of our knowledge, this is the first work on mining temporal high utility itemsets from data streams. The novel contribution of THUI-Mine is that it can effectively identify the temporal high utility itemsets by generating fewer candidate itemsets such that the execution time can be reduced substantially in mining all high utility itemsets in data streams. In this way, the process of discovering all temporal high utility itemsets under all time windows of data streams can be achieved effectively with less memory space and execution time. This meets the critical requirements on time and space efficiency for mining data streams. Through experimental evaluation, THUI-Mine is shown to significantly outperform other existing methods like Two-Phase algorithm under various experimental conditions.

Original languageEnglish
Pages (from-to)1105-1117
Number of pages13
JournalJournal of Systems and Software
Volume81
Issue number7
DOIs
StatePublished - 1 Jul 2008

Keywords

  • Association rules
  • Data stream mining
  • Temporal high utility itemsets
  • Utility mining

Fingerprint Dive into the research topics of 'An efficient algorithm for mining temporal high utility itemsets from data streams'. Together they form a unique fingerprint.

Cite this