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. ER - TY - JOUR AU - Stumme, Gerd AU - Taouil, Rafik AU - Bastide, Yves AU - Pasquier, Nicolas AU - Lakhal, Lotfi T1 - Computing iceberg concept lattices with TITANIC JO - Data & Knowledge Engineering PY - 2002/08 VL - 42 IS - 2 SP - 189 EP - 222 UR - http://portal.acm.org/citation.cfm?id=606457 DO - 10.1016/S0169-023X(02)00057-5 KW - titanic KW - concept KW - iceberg KW - fca KW - kdd KW - computing L1 - SN - N1 - N1 - AB - 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. ER - TY - JOUR AU - Stumme, Gerd AU - Taouil, Rafik AU - Bastide, Yves AU - Pasquier, Nicolas AU - Lakhal, Lotfi T1 - Computing iceberg concept lattices with TITANIC JO - Data & Knowledge Engineering PY - 2002/08 VL - 42 IS - 2 SP - 189 EP - 222 UR - http://portal.acm.org/citation.cfm?id=606457 DO - 10.1016/S0169-023X(02)00057-5 KW - lattice KW - titanic KW - concept KW - formal KW - iceberg KW - analysis KW - fca L1 - SN - N1 - N1 - AB - 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. ER - TY - JOUR AU - Stumme, Gerd AU - Taouil, Rafik AU - Bastide, Yves AU - Pasquier, Nicolas AU - Lakhal, Lotfi T1 - Computing iceberg concept lattices with TITANIC JO - Data Knowl. Eng. PY - 2002/ VL - 42 IS - 2 SP - 189 EP - 222 UR - DO - http://dx.doi.org/10.1016/S0169-023X(02)00057-5 KW - fca L1 - SN - N1 - Computing iceberg concept lattices with TITANIC N1 - AB - ER - TY - JOUR AU - Pasquier, Nicolas AU - Bastide, Yves AU - Taouil, Rafik AU - Lakhal, Lotfi T1 - Efficient mining of association rules using closed itemset lattices JO - Information Systems PY - 1999/ VL - 24 IS - 1 SP - 25 EP - 46 UR - http://www.sciencedirect.com/science/article/pii/S0306437999000034 DO - http://dx.doi.org/10.1016/S0306-4379(99)00003-4 KW - association KW - fca KW - rule L1 - SN - N1 - Efficient mining of association rules using closed itemset lattices N1 - AB - Discovering association rules is one of the most important task in data mining. Many efficient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning the subset lattice (itemset lattice). In this paper we propose an efficient algorithm, called Close, based on a new mining method: pruning the closed set lattice (closed itemset lattice). This lattice, which is a sub-order of the subset lattice, is closely related to Wille's concept lattice in formal concept analysis. Experiments comparing Close to an optimized version of Apriori showed that Close is very efficient for mining dense and/or correlated data such as census style data, and performs reasonably well for market basket style data. ER - TY - CONF AU - Pasquier, Nicolas AU - Bastide, Yves AU - Taouil, Rafik AU - Lakhal, Lotfi A2 - Bouzeghoub, Mokrane T1 - Pruning closed itemset lattices for associations rules. T2 - Bases de Donn�es Avanc�es PB - C1 - PY - 1998/ CY - VL - IS - SP - EP - UR - http://dblp.uni-trier.de/db/conf/bda/bda98.html#PasquierBTL98 DO - KW - rules KW - closed KW - concept KW - formal KW - association KW - OntologyHandbook KW - FCA KW - analysis KW - fca KW - itemset KW - mining L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Pasquier, Nicolas AU - Bastide, Yves AU - Taouil, Rafik AU - Lakhal, Lotfi A2 - Bouzeghoub, Mokrane T1 - Pruning closed itemset lattices for associations rules. T2 - Bases de Données Avancées PB - C1 - PY - 1998/ CY - VL - IS - SP - EP - UR - http://dblp.uni-trier.de/db/conf/bda/bda98.html#PasquierBTL98 DO - KW - rules KW - closed KW - concept KW - formal KW - association KW - analysis KW - fca KW - itemset KW - mining L1 - SN - N1 - N1 - AB - ER -