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Mining Several Kinds of Temporal Association Rules Enhanced by Tree Structures

author/s: Tim Schlüter, Stefan Conrad
booktitle:International Conference on Information, Process, and Knowledge Management (eKNOW 2010), Saint Maarten, Netherlands, Antilles, February 10-15, 2010
keywords:Knowledge Discovery in Databases, Market Basket Analysis, Temporal Association Rule Mining

Market basket analysis is one important application of knowledge discovery in databases. Real life market basket databases usually contain temporal coherences, which cannot be captured by means of standard association rule mining. Thus there is a need for developing algorithms, that reveal such temporal coherences within this data. This paper gathers several notions of temporal association rules and presents an approach for mining most of these kinds (cyclic, lifespan- and calendar-based) in a market basket database, enhanced by two novel tree structures. We called these two tree structures EP- and ET-Tree, which are derived from existing approaches improving standard association rule mining. They are used as representation of the database and thus make the discovery of temporal association rules very efficient.

Heinrich Heine Universität

Datenbanken und Informationssysteme


Prof. Dr. Stefan Conrad

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40225 Düsseldorf
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Sabine Freese

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Universitätsstr. 1
40225 Düsseldorf
Gebäude: 25.12
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Fax: +49 211 81-13463
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