| Abstract |
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In this paper we consider the problem of discovering sequential
patterns by handling time constraints. While sequential
patterns could be seen as temporal relationships
between facts embedded in the database, generalized sequential
patterns aim at providing the end user with a
more flexible handling of the transactions embedded in the
database. We propose a new efficient algorithm, called GTC
(Graph for Time Constraints) for mining such patterns in
very large databases. It is based on the idea that handling
time constraints in the earlier stage of the algorithm can be
highly beneficial since it minimizes computational costs by
preprocessing data sequences. Our test shows that the proposed
algorithm performs significantly faster than a stateof-
the-art sequence mining algorithm.
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 Additional Information
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Citation:
Florent Masseglia, Pascal Poncelet, Maguelonne Teisseire,
"Pre-Processing Time Constraints for Efficiently Mining Generalized Sequential Patterns,"
time,
pp. 87-95,
11th International Symposium on Temporal Representation and Reasoning (TIME'04),
2004
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