2011 TowardsBoundingSequentialPatter

• (Raïssi & Pei, 2011) ⇒ Chedy Raïssi, and Jian Pei. (2011). “Towards Bounding Sequential Patterns.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020612

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Cited By

• http://scholar.google.com/scholar?q=%222011%22+Towards+Bounding+Sequential+Patterns
• http://dl.acm.org/citation.cfm?id=2020408.2020612&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; combinatorics; counting problems; data mining; generating functions; sequential pattern mining; theory

Abstract

Given a sequence database, can we have a non-trivial upper bound on the number of sequential patterns? The problem of bounding sequential patterns is very challenging in theory due to the combinatorial complexity of sequences, even given some inspiring results on bounding itemsets in frequent itemset mining. Moreover, the problem is highly meaningful in practice, since the upper bound can be used in many applications such as space allocation in building sequence data warehouses. In this paper, we tackle the problem of bounding sequential patterns by presenting, for the first time in the field of sequential pattern mining, strong combinatorial results on computing the number of possible sequential patterns that can be generated at a given length k. We introduce, as a case study, two novel techniques to estimate the number of candidate sequences. An extensive empirical study on both real data and synthetic data verifies the effectiveness of our methods.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 TowardsBoundingSequentialPatter	Jian Pei Chedy Raïssi			Towards Bounding Sequential Patterns				10.1145/2020408.2020612		2011