Article98: Unterschied zwischen den Versionen
K (Added from ontology) |
K (Added from ontology) |
||
Zeile 1: | Zeile 1: | ||
− | {{Publikation | + | {{Publikation Erster Autor |
− | | | + | |ErsterAutorNachname=Stumme |
− | | | + | |ErsterAutorVorname=Gerd |
}} | }} | ||
{{Publikation Author | {{Publikation Author | ||
Zeile 8: | Zeile 8: | ||
}} | }} | ||
{{Publikation Author | {{Publikation Author | ||
− | |Rank= | + | |Rank=3 |
− | |Author= | + | |Author=Yves Bastide |
+ | }} | ||
+ | {{Publikation Author | ||
+ | |Rank=5 | ||
+ | |Author=Lotfi Lakhal | ||
}} | }} | ||
{{Publikation Author | {{Publikation Author | ||
|Rank=2 | |Rank=2 | ||
|Author=Rafik Taouil | |Author=Rafik Taouil | ||
− | |||
− | |||
− | |||
− | |||
}} | }} | ||
{{Article | {{Article |
Version vom 8. September 2009, 10:42 Uhr
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.
Download: Media:2002_98_Stumme_Computing_Icebe_1.pdf