Diploma Thesis DIP-1554

BibliographyRantzau, Ralf: Extended Concepts for Association Rule Discovery.
University of Stuttgart, Faculty of Computer Science, Diploma Thesis No. 1554 (1997).
61 pages, english.
CR-SchemaH.2.8 (Database Applications)
H.4.2 (Information Systems Applications Types of Systems)
Keywordsknowledge discovery; data mining; association rule discovery
Abstract

The aim of data mining is the discovery of patterns within data stored in databases. Mining for association rules is a data mining method that lends itself to formulating conditional statements such as "if customers buy product A then they also buy product B and C with a probability of 90 percent." We consider different extended concepts of basic association rules. One of these concepts, quantitative association rules, is discussed in detail. Quantitative association rules allow statements like "20 percent of customers who buy at least three units of product A also buy between five and ten units of B and two units of C." We show that the quantitative nature of data can be hidden from the algorithm that mines for association rules. Thus, any standard algorithm that solves the basic problem can be used to cope with quantitative association rules. The presentation of our approach includes the design of the database, algorithms and data structures, as well as experiments with a prototype implementation.

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Copyrightrrantzau@acm.org
Department(s)University of Stuttgart, Institute of Parallel and Distributed High-Performance Systems, Applications of Parallel and Distributed Systems (Prof. Reuter)
Project(s)CRITIKAL
Entry dateNovember 29, 2000
   Publ. Computer Science