Incremental update on sequential patterns in large databases

Mining of sequential patterns in a transactional database is time consuming due to its complexity. Maintaining present patterns is a non-trivial task after database update, since appended data sequences may invalidate old patterns and create new ones. In contrast to re-mining, the key to improve mining performance in the proposed incremental update algorithm is to effectively utilize the discovered knowledge. By counting over appended data sequences instead of the entire updated database in most cases, fast filtering of patterns found in last mining and successive reductions in candidate sequences together make efficient update on sequential patterns possible