Mining Approach for Updating Sequential Patterns
The authors are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. The discovery of frequent sequences in temporal databases is an important data mining problem. Most current work assumes that the database is static, and a database update requires rediscovering all the patterns by scanning the entire old and new database. They consider the problem of the incremental mining of sequential patterns when new transactions or new customers are added to an original database.