A soft set approach for association rules mining

In this paper, we present an alternative approach for mining regular association rules and maximal association rules from transactional datasets using soft set theory. This approach is started by a transformation of a transactional dataset into a Boolean-valued information system. Since the ''standard'' soft set deals with such information system, thus a transactional dataset can be represented as a soft set. Using the concept of parameters co-occurrence in a transaction, we define the notion of regular and maximal association rules between two sets of parameters, also their support, confidence and maximal support, maximal confidences, respectively properly using soft set theory. The results show that the soft regular and soft maximal association rules provide identical rules as compared to the regular and maximal association rules.

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

[2]  Eric C. C. Tsang,et al.  The parameterization reduction of soft sets and its applications , 2005 .

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

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

[5]  Yen-Liang Chen,et al.  Mining fuzzy association rules from questionnaire data , 2009, Knowl. Based Syst..

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

[7]  A. R. Roy,et al.  A fuzzy soft set theoretic approach to decision making problems , 2007 .

[8]  A. R. Roy,et al.  Soft set theory , 2003 .

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

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

[11]  Jiye Liang,et al.  Approximation reduction in inconsistent incomplete decision tables , 2010, Knowl. Based Syst..

[12]  Chien-Sing Lee,et al.  Processing online analytics with classification and association rule mining , 2010, Knowl. Based Syst..

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

[14]  Steven Li,et al.  The normal parameter reduction of soft sets and its algorithm , 2008, Comput. Math. Appl..

[15]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[16]  A. R. Roy,et al.  An application of soft sets in a decision making problem , 2002 .

[17]  Yan Zou,et al.  Data analysis approaches of soft sets under incomplete information , 2008, Knowl. Based Syst..

[18]  Jemal H. Abawajy,et al.  A rough set approach for selecting clustering attribute , 2010, Knowl. Based Syst..