Generalized Net of the Process of Sequential Pattern Mining by Generalized Sequential Pattern Algorithm (GSP)

Abstract

In the present paper is constructed a Generalized net model of a process of sequential pattern mining by a generalized sequential pattern algorithm. Sequence pattern mining is a technique used for predictive data mining. It is used for discovering of frequent sequences in the databases. A sequence is regarded as frequent when it occurs in the data above a previously user defined minimum support within the applied time constraints. The analysis is an extension of frequent pattern mining technique that extracts frequent itemsets. They can be used for the creation of association rules. GSP algorithm was realized with metrological observations from weather databases, infrared camera and smoke detector to determine the possibility of forest fire. The proposed Generated net model can be used to monitor the sequence pattern mining process depending on meteorological parameters.