Journal article

A binary decision diagram based approach for mining frequent subsequences

Elsa Loekito, James Bailey, Jian Pei

KNOWLEDGE AND INFORMATION SYSTEMS | SPRINGER LONDON LTD | Published : 2010

Abstract

Sequential pattern mining is an important problem in data mining. State of the art techniques for mining sequential patterns, such as frequent subsequences, are often based on the pattern-growth approach, which recursively projects conditional databases. Explicitly creating database projections is thought to be a major computational bottleneck, but we will show in this paper that it can be beneficial when the appropriate data structure is used. Our technique uses a canonical directed acyclic graph as the sequence database representation, which can be represented as a binary decision diagram (BDD). In this paper, we introduce a new type of BDD, namely a sequence BDD (SeqBDD), and show how it ..

View full abstract

Grants

Funding Acknowledgements

This paper was partially supported by NICTA. NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program.