IncSpan: incremental mining of sequential patterns in large database

Many real life sequence databases, such as customer shopping sequences, medical treatment sequences, etc., grow incrementally. It is undesirable to mine sequential patterns from scratch each time when a small set of sequences grow, or when some new sequences are added into the database. Incremental algorithm should be developed for sequential pattern mining so that mining can be adapted to frequent and incremental database updates, including both insertions and deletions. However, it is nontrivial to mine sequential patterns incrementally, especially when the existing sequences grow incrementally because such growth may lead to the generation of many new patterns due to the interactions of the growing subsequences with the original ones. In this study, we develop an efficient algorithm, IncSpan, for incremental mining of sequential patterns, by exploring some interesting properties. Our performance study shows that IncSpan outperforms some previously proposed incremental algorithms as...

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Hong Cheng, Xifeng Yan, Jiawei Han
• Keywords: Data Mining, Incremental Database Updates, Incremental Mining, KDD 2004, Sequential Pattern Mining