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Efficient Data Mining Based on Formal Concept Analysis

Published: 2002
Herausgeber: A. Hameurlain, R. Cicchetti, R. Traunmüller (eds.)
Buchtitel: Database and Expert Systems Applications. Proc. DEXA 2002
Ausgabe: 2453
Reihe: LNCS
Seiten: 534-546
Verlag: Springer

Referierte Veröffentlichung


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.

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