Efficient Mining of Association Rules Based on Formal Concept Analysis.
In:
B. Ganter, G. Stumme and R. Wille, editors,
Formal Concept Analysis, pages 180-195.
Springer Berlin Heidelberg, 2005.
Lotfi Lakhal and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
Association rules are a popular knowledge discovery technique for warehouse basket analysis. They indicate which items of the warehouse are frequently bought together. The problem of association rule mining has first been stated in 1993. Five years later, several research groups discovered that this problem has a strong connection to
Efficient Mining of Association Rules Based on Formal Concept Analysis.
2005.
Lotfi Lakhal and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
Association rules are a popular knowledge discovery technique for
warehouse basket analysis. They indicate which items of the
warehouse are frequently bought together. The problem of association
rule mining has first been stated in 1993. Five years later, several
research groups discovered that this problem has a strong connection
to Formal Concept Analysis (FCA). In this survey, we will first
introduce some basic ideas of this connection along a specific
algorithm, and show how FCA helps in reducing the number
of resulting rules without loss of information, before giving a
general overview over the history and state of the art of applying
FCA for association rule mining.
Efficient Mining of Association Rules Based on Formal Concept Analysis.
2005.
Lotfi Lakhal and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
Association rules are a popular knowledge discovery technique for
warehouse basket analysis. They indicate which items of the
warehouse are frequently bought together. The problem of association
rule mining has first been stated in 1993. Five years later, several
research groups discovered that this problem has a strong connection
to Formal Concept Analysis (FCA). In this survey, we will first
introduce some basic ideas of this connection along a specific
algorithm, and show how FCA helps in reducing the number
of resulting rules without loss of information, before giving a
general overview over the history and state of the art of applying
FCA for association rule mining.
Generating a Condensed Representation for Association Rules.
Journal Intelligent Information Systems (JIIS), 24(1):29-60, 2005.
Nicolas Pasquier, Rafik Taouil, Yves Bastide, Gerd Stumme and Lotfi Lakhal.
[doi]
[BibTeX]
Generating a Condensed Representation for Association Rules.
Journal Intelligent Information Systems (JIIS), 24(1):29-60, 2005.
Nicolas Pasquier, Rafik Taouil, Yves Bastide, Gerd Stumme and Lotfi Lakhal.
[doi]
[BibTeX]
Computing iceberg concept lattices with TITANIC.
Data & Knowledge Engineering, 42(2):189-222, 2002.
Gerd Stumme, Rafik Taouil, Yves Bastide, Nicolas Pasquier and Lotfi Lakhal.
[doi]
[abstract]
[BibTeX]
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 TITANIC.
Data & Knowledge Engineering, 42(2):189-222, 2002.
Gerd Stumme, Rafik Taouil, Yves Bastide, Nicolas Pasquier and Lotfi Lakhal.
[doi]
[abstract]
[BibTeX]
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 TITANIC.
Data & Knowledge Engineering, 42(2):189-222, 2002.
Gerd Stumme, Rafik Taouil, Yves Bastide, Nicolas Pasquier and Lotfi Lakhal.
[doi]
[abstract]
[BibTeX]
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 TITANIC.
Data Knowl. Eng., 42(2):189-222, 2002.
Gerd Stumme, Rafik Taouil, Yves Bastide, Nicolas Pasquier and Lotfi Lakhal.
[BibTeX]
Efficient mining of association rules using closed itemset lattices .
Information Systems , 24(1):25 - 46, 1999.
Nicolas Pasquier, Yves Bastide, Rafik Taouil and Lotfi Lakhal.
[doi]
[abstract]
[BibTeX]
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.
Pruning closed itemset lattices for associations rules..
In: M. Bouzeghoub, editor,
Bases de Donn�es Avanc�es.
1998.
Nicolas Pasquier, Yves Bastide, Rafik Taouil and Lotfi Lakhal.
[doi]
[BibTeX]
Pruning closed itemset lattices for associations rules..
In: M. Bouzeghoub, editor,
Bases de Données Avancées.
1998.
Nicolas Pasquier, Yves Bastide, Rafik Taouil and Lotfi Lakhal.
[doi]
[BibTeX]