A new method for mining Frequent Weighted Itemsets based on WIT-trees

The mining frequent itemsets plays an important role in the mining of association rules. Frequent itemsets are typically mined from binary databases where each item in a transaction may have a different significance. Mining Frequent Weighted Itemsets (FWI) from weighted items transaction databases addresses this issue. This paper therefore proposes algorithms for the fast mining of FWI from weighted item transaction databases. Firstly, an algorithm for directly mining FWI using WIT-trees is presented. After that, some theorems are developed concerning the fast mining of FWI. Based on these theorems, an advanced algorithm for mining FWI is proposed. Finally, a Diffset strategy for the efficient computation of the weighted support for itemsets is described, and an algorithm for mining FWI using Diffsets presented. A complete evaluation of the proposed algorithms is also presented.

[1]  Bay Vo,et al.  Interestingness measures for association rules: Combination between lattice and hash tables , 2011, Expert Syst. Appl..

[2]  Mohammed J. Zaki,et al.  Fast vertical mining using diffsets , 2003, KDD '03.

[3]  Tzung-Pei Hong,et al.  An efficient and effective association-rule maintenance algorithm for record modification , 2010, Expert Syst. Appl..

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

[5]  Huy Nguyen,et al.  A Novel Algorithm for Mining High Utility Itemsets , 2009, 2009 First Asian Conference on Intelligent Information and Database Systems.

[6]  Bay Vo,et al.  Mining minimal non-redundant association rules using frequent itemsets lattice , 2011, Int. J. Intell. Syst. Technol. Appl..

[7]  Mohammed J. Zaki.  Mining Non-Redundant Association Rules , 2004, Data Min. Knowl. Discov..

[8]  Fionn Murtagh,et al.  Weighted Association Rule Mining using weighted support and significance framework , 2003, KDD '03.

[9]  Mohammed J. Zaki,et al.  Efficient algorithms for mining closed itemsets and their lattice structure , 2005, IEEE Transactions on Knowledge and Data Engineering.

[10]  Philip S. Yu,et al.  Efficient mining of weighted association rules (WAR) , 2000, KDD '00.

[11]  Tzung-Pei Hong,et al.  DBV-Miner: A Dynamic Bit-Vector approach for fast mining frequent closed itemsets , 2012, Expert Syst. Appl..

[12]  M. Sulaiman Khan,et al.  Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework , 2009, PAKDD Workshops.

[13]  Ada Wai-Chee Fu,et al.  Mining association rules with weighted items , 1998, Proceedings. IDEAS'98. International Database Engineering and Applications Symposium (Cat. No.98EX156).

[14]  M. Sulaiman Khan,et al.  A Weighted Utility Framework for Mining Association Rules , 2008, 2008 Second UKSIM European Symposium on Computer Modeling and Simulation.

[15]  Huy Nguyen,et al.  An efficient strategy for mining high utility itemsets , 2011, Int. J. Intell. Inf. Database Syst..

[16]  Tzung-Pei Hong,et al.  An effective mining approach for up-to-date patterns , 2009, Expert Syst. Appl..

[17]  Bac Le,et al.  Mining traditional association rules using frequent itemsets lattice , 2009, 2009 International Conference on Computers & Industrial Engineering.

[18]  Tzung-Pei Hong,et al.  Incrementally fast updated frequent pattern trees , 2008, Expert Syst. Appl..

[19]  D. Ramkumar,et al.  Weighted Association Rules: Model and Algorithm , 1998 .

[20]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[21]  Tzung-Pei Hong,et al.  Maintenance of fast updated frequent pattern trees for record deletion , 2009, Comput. Stat. Data Anal..

[22]  Tzung-Pei Hong,et al.  The Pre-FUFP algorithm for incremental mining , 2009, Expert Syst. Appl..