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A Framework for Incremental Mining of Interesting Temporal Association Rules
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 131 - Number 8 |
| Year of Publication: 2015 |
| Authors: Ahmed Sultan Al-Hegami |
10.5120/ijca2015907433
|
Ahmed Sultan Al-Hegami . A Framework for Incremental Mining of Interesting Temporal Association Rules. International Journal of Computer Applications. 131, 8 ( December 2015), 28-33. DOI=10.5120/ijca2015907433
Abstract
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 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.
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