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. ER - TY - GEN AU - Lakhal, Lotfi AU - Stumme, Gerd A2 - Ganter, Bernhard A2 - Stumme, Gerd A2 - Wille, Rudolf T1 - Efficient Mining of Association Rules Based on Formal Concept Analysis JO - PB - Springer AD - Heidelberg PY - 2005/ VL - 3626 IS - SP - 180 EP - 195 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2005/lakhal2005efficient.pdf M3 - KW - concept KW - book KW - discovery KW - 2005 KW - association KW - l3s KW - analysis KW - kdd KW - myown KW - data KW - knowledge KW - closed KW - formal KW - rules KW - itegpub KW - condensed KW - itemsets KW - fca KW - representations KW - mining L1 - N1 - Publications of Gerd Stumme N1 - AB - 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. ER - TY - JOUR AU - Pasquier, Nicolas AU - Taouil, Rafik AU - Bastide, Yves AU - Stumme, Gerd AU - Lakhal, Lotfi T1 - Generating a Condensed Representation for Association Rules JO - Journal Intelligent Information Systems (JIIS) PY - 2005/ VL - 24 IS - 1 SP - 29 EP - 60 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2005/pasquier2005generating.pdf M3 - KW - concept KW - discovery KW - 2005 KW - association KW - OntologyHandbook KW - l3s KW - analysis KW - kdd KW - itemset KW - myown KW - rule KW - data KW - knowledge KW - closed KW - formal KW - rules KW - itegpub KW - condensed KW - fca KW - sets KW - representations KW - mining L1 - SN - N1 - alpha N1 - AB - ER - TY - JOUR AU - Pasquier, Nicolas AU - Taouil, Rafik AU - Bastide, Yves AU - Stumme, Gerd AU - Lakhal, Lotfi T1 - Generating a Condensed Representation for Association Rules JO - Journal Intelligent Information Systems (JIIS) PY - 2005/ VL - 24 IS - 1 SP - 29 EP - 60 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2005/pasquier2005generating.pdf M3 - KW - concept KW - discovery KW - 2005 KW - association KW - OntologyHandbook KW - l3s KW - analysis KW - kdd KW - itemset KW - myown KW - rule KW - data KW - knowledge KW - closed KW - formal KW - rules KW - itegpub KW - condensed KW - fca KW - sets KW - representations KW - mining L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - 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 M3 - 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 -