Maximal association rules: A tool for mining associations in text

Amihood Amir*, Yonatan Aumann, Ronen Feldman, Moshe Fresko

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


We describe a new tool for mining association rules, which is of special value in text mining. The new tool, called maximal associations, is geared toward discovering associations that are frequently lost when using regular association rules. Intuitively, a maximal association rule X ⇒max Y says that whenever X is the only item of its type in a transaction, than Y also appears, with some confidence. Maximal associations allow the discovery of associations pertaining to items that most often do not appear alone, but rather together with closely related items, and hence associations relevant only to these items tend to obtain low confidence. We provide a formal description of maximal association rules and efficient algorithms for discovering all such associations. We present the results of applying maximal association rules to two text corpora.

Original languageAmerican English
Pages (from-to)333-345
Number of pages13
JournalJournal of Intelligent Information Systems
Issue number3
StatePublished - Nov 2005
Externally publishedYes


  • Association rules
  • Data mining
  • Text mining


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