Lift-based search for significant dependencies in dense data sets

Authors:
W. Hämäläinen
Affiliations:
University of Helsinki, Finland
Venue:
Proceedings of the KDD-09 Workshop on Statistical and Relational Learning in Bioinformatics
Year:
2009

Citing 2
Cited 1

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficient Discovery of Statistically Significant Association Rules

ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining

Efficient Search Methods for Statistical Dependency Rules

Fundamenta Informaticae - Machine Learning in Bioinformatics

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Abstract

Dependency analysis is an important but computationally demanding problem in all empirical science. It is especially problematic in bioinformatics, where data sets are often high dimensional, dense and/or strongly correlated. As a solution, we introduce a new algorithm which searches the most significant association rules expressing positive dependencies. The algorithm uses several effective pruning principles, which enable search without any minimum frequency thresholds. According to our initial experiments, the algorithm suits especially well for typical biological and medical data sets.