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"id" : "https://puma.uni-kassel.de/bibtex/2fc31933f0eec502e305b6aecb9ef6e8a/stumme",
"tags" : [
"titanic","itegpub","icfca","l3s","fca","myown","citedBy:doerfel2012publication"
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"label" : "Computing iceberg concept lattices with TITANIC",
"user" : "stumme",
"description" : "",
"date" : "2013-03-18 14:06:44",
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"journal": "Data & Knowledge Engineering","publisher":"Elsevier Science Publishers B. V.","address":"Amsterdam, The Netherlands, The Netherlands",
"year": "2002",
"url": "http://portal.acm.org/citation.cfm?id=606457",
"author": [
"Gerd Stumme","Rafik Taouil","Yves Bastide","Nicolas Pasquier","Lotfi Lakhal"
],
"authors": [
{"first" : "Gerd", "last" : "Stumme"},
{"first" : "Rafik", "last" : "Taouil"},
{"first" : "Yves", "last" : "Bastide"},
{"first" : "Nicolas", "last" : "Pasquier"},
{"first" : "Lotfi", "last" : "Lakhal"}
],
"volume": "42","number": "2","pages": "189--222","abstract": "We introduce the notion of iceberg concept lattices\n and show their use in knowledge discovery in\n databases. Iceberg lattices are a conceptual\n clustering method, which is well suited for analyzing\n very large databases. They also serve as a condensed\n representation of frequent itemsets, as starting\n point for computing bases of association rules, and\n as a visualization method for association rules.\n Iceberg concept lattices are based on the theory of\n Formal Concept Analysis, a mathematical theory with\n applications in data analysis, information retrieval,\n and knowledge discovery. We present a new algorithm\n called TITANIC for computing (iceberg) concept\n lattices. It is based on data mining techniques with\n a level-wise approach. In fact, TITANIC can be used\n for a more general problem: Computing arbitrary\n closure systems when the closure operator comes along\n with a so-called weight function. The use of weight\n functions for computing closure systems has not been\n discussed in the literature up to now. Applications\n providing such a weight function include association\n rule mining, functional dependencies in databases,\n conceptual clustering, and ontology engineering. The\n algorithm is experimentally evaluated and compared\n with Ganter's Next-Closure algorithm. The evaluation\n shows an important gain in efficiency, especially for\n weakly correlated data.",
"bibtexKey": "stumme2002computing"
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