A Framework for Incremental Mining of Interesting Temporal Association Rules

Ahmed Sultan
2015 International Journal of Computer Applications  
Association rules are an important problem in data mining. Massively increasing volume of data with temporal dependencies in real life databases has motivated researchers to design novel and incremental algorithms for temporal association rules mining. In this paper, an incremental association rules mining algorithm is proposed that integrates interestingness criterion during the process of building the model called SUMA. One of the main features of the proposed framework is to capture the user
more » ... background knowledge, which is monotonically augmented. The incremental model that reflects the changing data over the time and the user beliefs is attractive in order to make the over all KDD process more effective and efficient. The proposed framework is implemented and experiment it with some public datasets and found the results quite promising. General Terms
doi:10.5120/ijca2015907433 fatcat:nv7rbchicnddrgz76hfwwq5rre