Progressive Mining of Sequential Patterns Based on Single Constraint

Abstract

Data that were appeared in the order of time and stored in a sequence database can be processed to obtain sequential patterns. Sequential pattern mining is the process to obtain sequential patterns from database. However, large amount of data with a variety of data type and rapid data growth raise the scalability issue in data mining process. On the other hand, user needs to analyze data based on specific organizational needs. Therefore, constraint is used to impose limitation in the mining process. Constraint in sequential pattern mining can reduce the short and trivial sequential patterns so that the sequential patterns satisfy user needs. Progressive mining of sequential patterns, PISA, based on single constraint utilizes Period of Interest (POI) as predefined time frame set by user in progressive sequential tree. Single constraint checking in PISA utilizes the concept of anti monotonic or monotonic constraint. Therefore, the number of sequential patterns will decrease, the total execution time of mining process will decrease and as a result, the system scalability will be achieved.