PUMA publications for /author/Rafik%20Taouil/titanichttps://puma.uni-kassel.de/author/Rafik%20Taouil/titanicPUMA RSS feed for /author/Rafik%20Taouil/titanic2021-01-17T20:43:06+01:00Computing iceberg concept lattices with TITANIChttps://puma.uni-kassel.de/bibtex/2fc31933f0eec502e305b6aecb9ef6e8a/stummestumme2013-03-18T14:06:44+01:00titanic itegpub icfca l3s fca myown citedBy:doerfel2012publication <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Rafik Taouil" itemprop="url" href="/author/Rafik%20Taouil"><span itemprop="name">R. Taouil</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yves Bastide" itemprop="url" href="/author/Yves%20Bastide"><span itemprop="name">Y. Bastide</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nicolas Pasquier" itemprop="url" href="/author/Nicolas%20Pasquier"><span itemprop="name">N. Pasquier</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lotfi Lakhal" itemprop="url" href="/author/Lotfi%20Lakhal"><span itemprop="name">L. Lakhal</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Data & Knowledge Engineering</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">42 </span></span>(<span itemprop="issueNumber">2</span>):
<span itemprop="pagination">189--222</span></em> </span>(<em><span>2002<meta content="2002" itemprop="datePublished"/></span></em>)Mon Mar 18 14:06:44 CET 2013Amsterdam, The Netherlands, The NetherlandsData \& Knowledge Engineering2189--222Computing iceberg concept lattices with TITANIC422002titanic itegpub icfca l3s fca myown citedBy:doerfel2012publication We introduce the notion of iceberg concept lattices
and show their use in knowledge discovery in
databases. 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. The use of weight
functions for computing closure systems has not been
discussed in the literature up to now. 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 Ganter's Next-Closure algorithm. The evaluation
shows an important gain in efficiency, especially for
weakly correlated data.Computing iceberg concept lattices with TITANIChttps://puma.uni-kassel.de/bibtex/2fc31933f0eec502e305b6aecb9ef6e8a/stephandoerfelstephandoerfel2011-01-19T15:17:53+01:00titanic concept iceberg fca kdd computing <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Rafik Taouil" itemprop="url" href="/author/Rafik%20Taouil"><span itemprop="name">R. Taouil</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yves Bastide" itemprop="url" href="/author/Yves%20Bastide"><span itemprop="name">Y. Bastide</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nicolas Pasquier" itemprop="url" href="/author/Nicolas%20Pasquier"><span itemprop="name">N. Pasquier</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lotfi Lakhal" itemprop="url" href="/author/Lotfi%20Lakhal"><span itemprop="name">L. Lakhal</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Data & Knowledge Engineering</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">42 </span></span>(<span itemprop="issueNumber">2</span>):
<span itemprop="pagination">189--222</span></em> </span>(<em><span>August 2002<meta content="August 2002" itemprop="datePublished"/></span></em>)Wed Jan 19 15:17:53 CET 2011Amsterdam, The Netherlands, The NetherlandsData & Knowledge Engineeringaug2189--222Computing iceberg concept lattices with TITANIC422002titanic concept iceberg fca kdd computing We introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. 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. The use of weight functions for computing closure systems has not been discussed in the literature up to now. 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 Ganter's Next-Closure algorithm. The evaluation shows an important gain in efficiency, especially for weakly correlated data.Computing iceberg concept lattices with TITANIChttps://puma.uni-kassel.de/bibtex/2fc31933f0eec502e305b6aecb9ef6e8a/jaeschkejaeschke2010-06-30T09:35:22+02:00lattice titanic concept formal iceberg analysis fca <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Rafik Taouil" itemprop="url" href="/author/Rafik%20Taouil"><span itemprop="name">R. Taouil</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yves Bastide" itemprop="url" href="/author/Yves%20Bastide"><span itemprop="name">Y. Bastide</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nicolas Pasquier" itemprop="url" href="/author/Nicolas%20Pasquier"><span itemprop="name">N. Pasquier</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lotfi Lakhal" itemprop="url" href="/author/Lotfi%20Lakhal"><span itemprop="name">L. Lakhal</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Data & Knowledge Engineering</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">42 </span></span>(<span itemprop="issueNumber">2</span>):
<span itemprop="pagination">189--222</span></em> </span>(<em><span>August 2002<meta content="August 2002" itemprop="datePublished"/></span></em>)Wed Jun 30 09:35:22 CEST 2010Amsterdam, The Netherlands, The NetherlandsData \& Knowledge Engineeringaug2189--222Computing iceberg concept lattices with TITANIC422002lattice titanic concept formal iceberg analysis fca We introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. 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. The use of weight functions for computing closure systems has not been discussed in the literature up to now. 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 Ganter's Next-Closure algorithm. The evaluation shows an important gain in efficiency, especially for weakly correlated data.