Efficiently mining Maximal Frequent Sets in dense databases for discovering association rules

Authors:
Krishnamoorthy Srikumar;Bharat Bhasker
Affiliations:
(Correspd.) Indian Institute of Management, Prabandh Nagar, Lucknow, 226 013, India. Tel.: +91 0522 2734101 to +91 0522 2734123/ Fax: +91 0522 2734025/ E-mail: srikumar@iiml.ac.in, bhasker@iiml.ac ...;Indian Institute of Management, Prabandh Nagar, Lucknow, 226 013, India. Tel.: +91 0522 2734101 to +91 0522 2734123/ Fax: +91 0522 2734025/ E-mail: srikumar@iiml.ac.in, bhasker@iiml.ac.in
Venue:
Intelligent Data Analysis
Year:
2004

Citing 11
Cited 0

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data

SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules

Advances in knowledge discovery and data mining
Efficiently mining long patterns from databases

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Depth first generation of long patterns

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Database Mining: A Performance Perspective

IEEE Transactions on Knowledge and Data Engineering
Pincer Search: A New Algorithm for Discovering the Maximum Frequent Set

EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Discovering All Most Specific Sentences by Randomized Algorithms

ICDT '97 Proceedings of the 6th International Conference on Database Theory
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases

Proceedings of the 17th International Conference on Data Engineering
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
MaxDomino: efficiently mining maximal sets

BNCOD'03 Proceedings of the 20th British national conference on Databases

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present, MaxDomino, an algorithm for mining Maximal Frequent Sets (MFS) for discovering association rules in dense databases. The algorithm uses novel concepts of dominancy factor and collapsibility of transaction for efficiently mining MFS. Unlike traditional bottom up approach with look-aheads, MaxDomino employs a top down strategy with selective bottom-up search for mining MFS. Using a set of benchmark dense datasets-created by University of California, Irvine-we demonstrate that MaxDomino outperforms GenMax-that performs better compared to other known algorithms-at higher support levels. Our algorithm is especially efficient for dense databases.