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Erschienen in:
2008 | OriginalPaper | Buchkapitel
RP-Tree: A Tree Structure to Discover Regular Patterns in Transactional Database
verfasst von : Syed Khairuzzaman Tanbeer, Chowdhury Farhan Ahmed, Byeong-Soo Jeong, Young-Koo Lee
Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2008
Verlag: Springer Berlin Heidelberg
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Temporal regularity of pattern appearance can be regarded as an important criterion for measuring the interestingness in several applications like market basket analysis, web administration, gene data analysis, network monitoring, and stock market. Even though there have been some efforts to discover
periodic
patterns in time-series and sequential data, none of the existing works is appropriate for discovering the patterns that occur regularly in a transactional database. Therefore, in this paper, we introduce a novel concept of mining
regular
patterns from transactional databases and propose an efficient data structure, called Regular Pattern tree (RP-tree in short), that enables a pattern growth-based mining technique to generate the complete set of
regular
patterns in a database for a user-given
regularity
threshold. Our comprehensive experimental study shows that RP-tree is both time and memory efficient in finding
regular
pattern.