Incremental mining of closed inter-transaction itemsets over data stream sliding windows

Shih Chuan Chiu, Hua Fu Li, Jiun-Long Huang, Hsin Han You

Research output: Contribution to journalArticle

6 Scopus citations


Mining inter-transaction association rules is one of the most interesting issues in data mining research. However, in a data stream environment the previous approaches are unable to find the result of the new-incoming data and the original database without re-computing the whole database. In this paper, we propose an incremental mining algorithm, called DSM-CITI (Data Stream Mining for Closed Inter-Transaction Itemsets), for discovering the set of all frequent inter-transaction itemsets from data streams. In the framework of DSM-CITI, a new in-memory summary data structure, ITP-tree, is developed to maintain frequent inter-transaction itemsets. Moreover, algorithm DSM-CITI is able to construct ITP-tree incrementally and uses the property to avoid unnecessary updates. Experimental studies show that the proposed algorithm is efficient and scalable for mining frequent inter-transaction itemsets over stream sliding windows.

Original languageEnglish
Pages (from-to)208-220
Number of pages13
JournalJournal of Information Science
Issue number2
StatePublished - 1 Apr 2011


  • data mining
  • data streams
  • frequent inter-transaction itemsets
  • incremental mining
  • stream sliding window mining

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