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Author(s): C. Berberidis, L. Angelis, I. Vlahavas.
Title: “PREVENT: An algorithm for mining inter-transactional patterns for the prediction of rare events”.
Availability:
Click here to download the PDF (Acrobat Reader) file (9 pages).
Keywords:
Data Mining, Rare events prediction, Association Rules, Sequence Analysis.
Appeared in:
2nd European Starting AI Researcher Symposium (STAIRS' 04), IOS Press, pp. 128-136, Valencia, Spain, 2004. Abstract: In this paper we propose a data mining technique for the efficient prediction of rare events, such as heat waves, network intrusions and engine failures, using inter transactional patterns. Data mining is a research area that attempts to assist the decision makers with a set of tools to treat a wide range of real world problems that the traditional statistical and mathematical approaches are not enough in terms of ef-ficiency and computational performance. Transaction databases, such as the ones in this paper that contain sets of events, require special approaches in order to extract valuable temporal knowledge. We utilize the framework of inter-transaction associa-tion rules, which associate events across a window of transactions. We propose an approach that extends sequential analysis to predict rare events in transaction data-bases. We formulate the problem of rare events prediction and we propose PREVENT, an algorithm that produces inter-transactional patterns for the fast and accurate prediction of a user-specified rare event. Finally, we provide experimental results and suggest some ideas for future research.
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