Efficient mining of temporal emerging itemsets from data streams

Chun Jung Chu, S. Tseng*, Tyne Liang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

In this paper, we propose a new method, namely EFI-Mine, for mining temporal emerging frequent itemsets from data streams efficiently and effectively. The temporal emerging frequent itemsets are those that are infrequent in the current time window of data stream but have high potential to become frequent in the subsequent time windows. Discovery of emerging frequent itemsets is an important process for mining interesting patterns like association rules from data streams. The novel contribution of EFI-Mine is that it can effectively identify the potential emerging itemsets such that the execution time can be reduced substantially in mining all frequent itemsets in data streams. This meets the critical requirements of time and space efficiency for mining data streams. The experimental results show that EFI-Mine can find the emerging frequent itemsets with high precision under different experimental conditions and it performs scalable in terms of execution time.

Original languageEnglish
Pages (from-to)885-893
Number of pages9
JournalExpert Systems with Applications
Volume36
Issue number1
DOIs
StatePublished - 1 Jan 2009

Keywords

  • Association rules
  • Data streams
  • Temporal emerging frequent itemsets

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