Multi-level sequence mining based on gsp

Abstract

Mining sequential patterns is an important problem in the field of data mining and many algorithms and optimization techniques have been published to deal with that problem. The GSP algorithm, which is one of them, can be used for mining sequential patterns with some additional constraints. In this paper, we propose a new algorithm for mining multi-level sequential patterns based on GSP. The idea is that if a more general item appears in a pattern, the pattern has higher or at least the same support as the one containing the corresponding specific item. However, too generalized sequence patterns are not important for user. In our algorithm generalization uses a selective method based on information content of patterns. This allows us to mine more patterns with the same minimal support threshold and to reveal new potentially useful patterns.

PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

“…AprioriAll algorithm creates candidate sequences in each step and calculates the support for each candidate sequence in the database. And then, it excludes the candidate sequences that fail to meet the minimum support and thereby makes frequent sequences [17][18][19]. Generally, the method of finding the top k items with simultaneous occurrence is to scan all sequences.…”