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Publication Details

  Author(s): C. Berberidis, I. Vlahavas.

Title: “Detection and Prediction of Rare Events in Transaction Databases”.

Availability: Click here to download the PDF (Acrobat Reader) file (20 pages).

Keywords: rare events, prediction, data mining, sequence mining.

Appeared in: International Journal on Artificial Intelligence Tools, World Scientific Publishing Company, 16(5), pp. 829 - 848, 2007.

Abstract: Rare events analysis is an area that includes methods for the detection and prediction of events, e.g. a network intrusion or an engine failure, that occur infrequently and have some impact to the system. There are various methods from the areas of statistics and data mining or that purpose. In this article we propose PREVENT, an algorithm which uses inter-transactional patterns for the prediction of rare events in transaction databases. PREVENT is a general purpose inter-transaction association rules mining algorithm that optimally fits the demands of rare event prediction. It requires only 1 scan on the original database and 2 over the transformed, which is considerably smaller and it is complete as it does not miss any patterns. We provide the mathematical formulation of the problem and experimental results that show PREVENT’s efficiency in terms of run time and effectiveness in terms of sensitivity and specificity.

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