In this paper we consider the problem of discovering sequential patterns by handling time constraints as defined in the Gsp algorithm. While sequential patterns could be seen as temporal relationships between facts embedded in the database where considered facts are merely characteristics of individuals or observations of individual behavior, generalized sequential patterns aim at providing the end user with a more flexible handling of the transactions embedded in the database. We thus 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 data mining process can be highly beneficial. One of the most significant new feature of our approach is that handling of time constraint can be easily taken into account in traditional levelwise approaches since it is carried out prior to and separately from the counting step of a data sequence. Our test shows that the proposed algorithm performs significantly faster than a state-of-the-art sequence mining algorithm.
However, REF proposed a more efficient algorithm than GSP known as 'Graph for Time Constraint' (GTC) which handled time constraints prior to and separately from the counting step of the data sequence, thus making it perform fastest.
Efficient mining of sequential patterns with time constraints: Reducing the combinations
Expert Syst. Appl.
Expert Syst. Appl.
Computer Science