YAMI: Incremental Mining of Interesting Association Patterns

Eiad Yafi1, Ahmed Sultan Al-Hegami2, Afshar Alam1, and Ranjit Biswas3
1Department of Computer Science, Jamia Hamdard University, India
2Faculty of Computers and Information Technology, Sana’a University, Yemen
3Department of Computer Engineering, Jadavpur University, India

 

Abstract: Association rules are an important problem in data mining. Massively increasing volume of data in real life databases has motivated researchers to design novel and incremental algorithms for association rules mining. In this paper, we propose an incremental association rules mining algorithm that integrates shocking interestingness criterion during the process of building the model. A new interesting measure called shocking measure is introduced. One of the main features of the proposed approach is to capture the user background knowledge, which is monotonically augmented. The incremental model that reflects the changing data and the user beliefs is attractive in order to make the over all KDD process more effective and efficient. We implemented the proposed approach and experiment it with some public datasets and found the results quite promising.

  

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