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dc.rightsUrheberrechtlich geschützt
dc.titleEfficient data mining based on formal concept analysiseng
dcterms.abstractFormal 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.eng
dcterms.accessRightsopen access
dcterms.creatorStumme, Gerd
dc.description.everythingAuch 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
dc.subject.swdFormale Begriffsanalyseger
dc.subject.swdData Miningger

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