Incremental Mining of Sequential Patterns Using Weights

Abstract

Real life sequential databases are usually not static. They tend to grow incrementally. So after every update a frequent pattern may no longer remains frequent while some infrequent patterns may appear as frequent in updated database. It is not a good idea to mine sequential database from scratch every time as the update occurs. It would be better if we can use the knowledge of already mined sequential patterns to find the complete set of sequential patterns for updated database. An incremental mining algorithm does the same thing. The main goal of an incremental mining algorithm is to reduce the time taken to find out the frequent patterns significantly i.e. it should mine the set of frequent patterns in significantly less time than a non-incremental mining algorithm. In this work we have approached using weight constraints, in time and space, of an idea of already existing algorithm called WSM.