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YAMI: Incremental Mining of Interesting Association Patterns

2010
Journal from gdlhub / 2017-08-14 11:52:31
Oleh : Eiad Yafi, Ahmed Sultan Al-Hegami, Afshar Alam , Ranjit Biswas, IAJIT
Dibuat : 2012-06-22, dengan 1 file

Keyword : Knowledge Discovery in Databases (KDD), data mining, incremental association rules, domain knowledge, interestingness, and Shocking Rules (SHR).
Subjek : YAMI: Incremental Mining of Interesting Association Patterns
Url : http://www.ccis2k.org/iajit/PDF/vol.9,no.6/2802-2.pdf
Sumber pengambilan dokumen : Internet

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.

Deskripsi Alternatif :

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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