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/ 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 - itegpub KW - icfca KW - l3s KW - fca KW - myown KW - citedBy:doerfel2012publication 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 -