Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2007

Abstract

Extensive research for frequent-pattern mining in the past decade has brought forth a number of pattern mining algorithms that are both effective and efficient. However, the existing frequent-pattern mining algorithms encounter challenges at mining rather large patterns, called colossal frequent patterns, in the presence of an explosive number of frequent patterns. Colossal patterns are critical to many applications, especially in domains like bioinformatics. In this study, we investigate a novel mining approach called Pattern-Fusion to efficiently find a good approximation to the colossal patterns. With Pattern-Fusion, a colossal pattern is discovered by fusing its small core patterns in one step, whereas the incremental pattern-growth mining strategies, such as those adopted in Apriori and FP-growth, have to examine a large number of mid-sized ones. This property distinguishes Pattern-Fusion from all the existing frequent pattern mining approaches and draws a new mining methodology. Our empirical studies show that, in cases where current mining algorithms cannot proceed, Pattern-Fusion is able to mine a result set which is a close enough approximation to the complete set of the colossal patterns, under a quality evaluation model proposed in this paper.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Publication

IEEE 23rd International Conference on Data Engineering 2007: 15 - 20 April, Istanbul, Turkey: Proceedings

First Page

706

Last Page

715

ISBN

9781424408023

Identifier

10.1109/ICDE.2007.367916

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Additional URL

https://doi.ieeecomputersociety.org/10.1109/ICDE.2007.367916

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