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Mining Sequential Patterns in Large Datasets

Authors: Xiaoyu Chang; Chunguang Zhou; Zhe Wang 0007; Yanwen Li; Ping Hu;

Mining Sequential Patterns in Large Datasets

Abstract

A novel algorithm FFSPAN (Fast Frequent Sequential Pattern mining algorithm) is proposed in this paper. FFSPAN mines all the frequent sequential patterns in large datasets, and solves the problem of searching frequent sequences in a sequence database by searching frequent items or frequent itemsets. Moreover, the databases that FFSPAN scans keep shrinking quickly, which makes the algorithm more efficient when the sequential patterns are longer. Experiments on standard test data show that FFSPAN is very effective.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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