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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 userdoi:10.5120/ijca2015907433 fatcat:nv7rbchicnddrgz76hfwwq5rre