MLKD logo   Machine Learning &
Knowledge Discovery Group

Publication Details

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

Title: “Inter-Transaction Association Rules Mining for Rare Events Prediction”.

Availability: Click here to download the PDF (Acrobat Reader) file (10 pages).

Keywords: Data Mining, Prediction.

Appeared in: Proc. 3rd Hellenic Conference on Artificial Intellligence (SETN '04), Samos, Greece, 2004.

Abstract: Rare events prediction is a very interesting and critical issue that has been approached within various contexts by research areas, such as statistics and machine learning. Data mining has provided a set of tools to treat this prob-lem when the size as well as the inherent features of the data, such as noise, randomness and special data types, become an issue for the traditional methods. Transaction databases that contain sets of events require special approaches in order to extract valuable temporal knowledge. Sequential analysis aims to dis-cover patterns or rules describing the temporal structure of data. In this paper we propose an approach that extends sequential analysis to predict rare events in transaction databases. We utilize the framework of inter-transaction associa-tion rules, which associate events across a window of transactions. The pro-posed algorithm produces rules for the accurate and timely prediction of a user-specified rare event, such as a network intrusion or an engine failure.

Relevant Links: