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Journal of Intelligent Information Systems, 25:3, 333345, 2005 c 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands.
 

Summary: Journal of Intelligent Information Systems, 25:3, 333345, 2005
c 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands.
Maximal Association Rules: A Tool for Mining
Associations in Text
AMIHOOD AMIR amir@cs.biu.ac.il
YONATAN AUMANN aumann@cs.biu.ac.il
RONEN FELDMAN feldman@cs.biu.ac.il
MOSHE FRESKO freskom1@cs.biu.ac.il
Department of Computer Science, Bar Ilan University, Ramat Gan, 52900, Israel
Received September 24, 2003; Revised September 26, 2004; Accepted September 28, 2004
Abstract. 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.

  

Source: Aumann, Yonatan - Computer Science Department, Bar Ilan University

 

Collections: Computer Technologies and Information Sciences