Fast and Effective Generation of Candidate-Sequences for Sequential Pattern Mining

The existing sequential pattern mining algorithms fall into two categories. One is the candidate-generation-and-test approach such as GSP, and the other is the pattern-growth approach such as PrefixSpan. Both GSP and PrefixSpan require setting the minimum support before their execution. We propose a new approach, called fast and effective generation of candidate-sequences (FEGC), to mine sequential patterns without predetermining the minimum support threshold. The main contribution is to scan all transactions in the database once and generate all the subsequences with their support counters. The experiments show that our algorithm performs well in various datasets.