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Computing Iceberg Concept Lattices with Titanic
Computing Iceberg Concept Lattices with Titanic
Veröffentlicht: 2002
Journal: Journal on Knowledge and Data Engineering (KDE)
Nummer: 2
Seiten: 189-222
Volume: 42
Referierte Veröffentlichung
Kurzfassung
We introduce the notion of iceberg concept
lattices and show their use in Knowledge Discovery in Databases
(KDD). Iceberg lattices are a conceptual clustering method, which
is well suited for analyzing very large databases. They also serve
as a condensed representation of frequent itemsets, as starting
point for computing bases of association rules, and as a
visualization method for association rules. Iceberg concept
lattices are based on the theory of Formal Concept Analysis, a
mathematical theory with applications in data analysis,
information retrieval, and knowledge discovery.
We present a new algorithm called Titanic for computing (iceberg)
concept lattices. It is based on data mining techniques with a
level-wise approach. In fact, Titanic can be used for a more
general problem: Computing arbitrary closure systems when the
closure operator comes along with a so-called weight function.
Applications providing such a weight function include association
rule mining, functional dependencies in databases, conceptual
clustering, and ontology engineering. The algorithm is
experimentally evaluated and compared with B. Ganter's
Next-Closure algorithm. The evaluation shows an important gain in
efficiency, especially for weakly correlated data.
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