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Efficient data mining based on formal concept analysis
Abstract
Formal Concept Analysis is an unsupervised learning technique for conceptual clustering. We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). Iceberg lattices are designed for analyzing very large databases. In particular they serve as a condensed representation of frequent patterns as known from association rule mining. In order to show the interplay between Formal Concept Analysis and association rule mining, we discuss the algorithm TITANIC. We show that iceberg concept lattices are a starting point for computing condensed sets of association rules without loss of information, and are a visualization method for the resulting rules.
Citation
@article{urn:nbn:de:hebis:34-2009022726467,
author={Stumme, Gerd},
title={Efficient data mining based on formal concept analysis},
year={2002}
}
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2009-02-27T12:11:40Z 2009-02-27T12:11:40Z 2002 urn:nbn:de:hebis:34-2009022726467 http://hdl.handle.net/123456789/2009022726467 221490 bytes application/pdf eng Urheberrechtlich geschützt https://rightsstatements.org/page/InC/1.0/ 004 Efficient data mining based on formal concept analysis Preprint Formal Concept Analysis is an unsupervised learning technique for conceptual clustering. We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). Iceberg lattices are designed for analyzing very large databases. In particular they serve as a condensed representation of frequent patterns as known from association rule mining. In order to show the interplay between Formal Concept Analysis and association rule mining, we discuss the algorithm TITANIC. We show that iceberg concept lattices are a starting point for computing condensed sets of association rules without loss of information, and are a visualization method for the resulting rules. open access Stumme, Gerd Extern Auch erschienen in: Hameurlain, Abdelkader u.a. (Hrsg.): Database and expert systems applications. (Lecture notes in computer science ; 2453). Berlin u.a. : Springer, 2002. S. 534-546. ISBN 3-540-44126-3 (The original publication is available at www.springerlink.com) Formale Begriffsanalyse Data Mining
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