Soft Set Approach for Maximal Association Rules Mining

In this paper, an alternative approach for maximal association rules mining from a transactional database using soft set theory is proposed. The first step of the proposed approach is based on representing a transactional database as a soft set. Based on the soft set, the notion of items co-occurrence in a transaction can be defined. The definitions of soft maximal association rules, maximal support and maximal confidence are presented using the concept of items co-occurrence. It is shown that by using soft set theory, maximal rules discovered are identical and faster as compared to traditional maximal and rough maximal association rules approaches.

[1]  D. Molodtsov.  Soft set theory—First results , 1999 .

[2]  Yaxin Bi,et al.  A rough set model with ontologies for discovering maximal association rules in document collections , 2003, Knowl. Based Syst..

[3]  David A. Bell,et al.  The rough set approach to association rule mining , 2003, Third IEEE International Conference on Data Mining.

[4]  Yonatan Aumann,et al.  Maximal Association Rules: A Tool for Mining Associations in Text , 2005, Journal of Intelligent Information Systems.

[5]  D. A. Bell,et al.  Mining Association Rules with Rough Sets , 2005, Intelligent Data Mining.

[6]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[7]  David J. Evans,et al.  Association rules on significant rare data using second support , 2006, Int. J. Comput. Math..

[8]  Andrzej Skowron,et al.  Rudiments of rough sets , 2007, Inf. Sci..

[9]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[10]  Mustafa Mat Deris,et al.  A Direct Proof of Every Rough Set is a Soft Set , 2009, 2009 Third Asia International Conference on Modelling & Simulation.

[11]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[12]  Yehuda Lindell,et al.  Text Mining at the Term Level , 1998, PKDD.

[13]  Yonatan Aumann,et al.  Maximal Association Rules: A New Tool for Mining for Keyword Co-Occurrences in Document Collections , 1997, KDD.