Incremental Sequence Mining
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. They consider the problem of the incremental mining of sequential patterns when new transactions or new customers are added to an original database. In this paper, they propose novel techniques for maintaining sequences in the presence of database updates and user interaction (e.g. modifying mining parameters). This is a very challenging task, since such updates can invalidate existing sequences or introduce new ones. In both the above scenarios, they avoid re-executing the algorithm on the entire dataset, thereby reducing execution time.