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Computing Iceberg Concept Lattices with TITANIC
, 2002
"... We introduce the notion of iceberg concept lattices... ..."
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Cited by 112 (15 self)
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We introduce the notion of iceberg concept lattices...
A systematic approach to the assessment of fuzzy association rules. Data Mining and Knowledge Discovery
, 2006
"... In order to allow for the analysis of data sets including numerical attributes, several generalizations of association rule mining based on fuzzy sets have been proposed in the literature. While the formal specification of fuzzy associations is more or less straightforward, the assessment of such ru ..."
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Cited by 43 (6 self)
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In order to allow for the analysis of data sets including numerical attributes, several generalizations of association rule mining based on fuzzy sets have been proposed in the literature. While the formal specification of fuzzy associations is more or less straightforward, the assessment of such rules by means of appropriate quality measures is less obvious. Particularly, it assumes an understanding of the semantic meaning of a fuzzy rule. This aspect has been ignored by most existing proposals, which must therefore be considered as adhoc to some extent. In this paper, we develop a systematic approach to the assessment of fuzzy association rules. To this end, we proceed from the idea of partitioning the data stored in a database into examples of a given rule, counterexamples, and irrelevant data. Evaluation measures are then derived from the cardinalities of the corresponding subsets. The problem of finding a proper partition has a rather obvious solution for standard association rules but becomes less trivial in the fuzzy case. Our results not only provide a sound justification for commonly used measures but also suggest a means for constructing meaningful alternatives. 1.
Discovering Shared Conceptualizations in Folksonomies
"... Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualisations are not formalised, but rather implicit. We present a new data mining task, the \emph{mining of all frequen ..."
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Cited by 27 (0 self)
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Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualisations are not formalised, but rather implicit. We present a new data mining task, the \emph{mining of all frequent triconcepts}, together with an efficient algorithm, for discovering these implicit shared conceptualisations. Our approach extends the data mining task of discovering all closed itemsets to threedimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large realworld examples.
Conceptual Clustering with Iceberg Concept Lattices
 In: Proc. of GIFachgruppentreffen Maschinelles Lernen'01, Universität Dortmund
, 2001
"... We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). 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 start ..."
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Cited by 16 (3 self)
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We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). 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.
Efficient Data Mining Based on Formal Concept Analysis
 In: A. Hameurlain, R. Cicchetti and R. Traunmller (Eds.), Proc. DEXA
, 2002
"... Formal Concept Analysis is an unsupervised learning technique for conceptual clustering. We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). Iceberg lattices are designed for analyzing very large databases. ..."
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Cited by 16 (1 self)
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Formal Concept Analysis is an unsupervised learning technique for conceptual clustering. We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). Iceberg lattices are designed for analyzing very large databases.
Formal Concept Analysis on its Way from Mathematics to Computer Science
 Proc. 10th Intl. Conf. on Conceptual Structures (ICCS 2002). LNCS
, 2002
"... In the last years, the main orientation of Formal Concept Analysis (FCA) has turned from mathematics towards computer science. ..."
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Cited by 13 (1 self)
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In the last years, the main orientation of Formal Concept Analysis (FCA) has turned from mathematics towards computer science.
Efficient mining of association rules based on formal concept analysis
 Formal Concept Analysis, volume 3626 of LNCS
, 2005
"... Abstract. 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 discover ..."
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Abstract. 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, Titanic, 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. 1
Iceberg query lattices for Datalog
 Conceptual Structures at Work, volume 3127 of Lecture
"... Abstract. In this paper we study two orthogonal extensions of the classical data mining problem of mining association rules, and show how they naturally interact. The first is the extension from a propositional representation to datalog, and the second is the condensed representation of frequent ite ..."
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Abstract. In this paper we study two orthogonal extensions of the classical data mining problem of mining association rules, and show how they naturally interact. The first is the extension from a propositional representation to datalog, and the second is the condensed representation of frequent itemsets by means of Formal Concept Analysis (FCA). We combine the notion of frequent datalog queries with iceberg concept lattices (also called closed itemsets) of FCA and introduce two kinds of iceberg query lattices as condensed representations of frequent datalog queries. We demonstrate that iceberg query lattices provide a natural way to visualize relational association rules in a nonredundant way. 1
Conceptual knowledge discovery – a humancentered approach
 Journal of Applied Artificial Intelligence
"... In this paper we discuss Conceptual Knowledge Discovery in Databases (CKDD) as it is developing in the field of Conceptual Knowledge Processing. Conceptual Knowledge Processing is based on the mathematical theory of Formal Concept Analysis which has become a successful theory for data analysis durin ..."
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Cited by 5 (1 self)
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In this paper we discuss Conceptual Knowledge Discovery in Databases (CKDD) as it is developing in the field of Conceptual Knowledge Processing. Conceptual Knowledge Processing is based on the mathematical theory of Formal Concept Analysis which has become a successful theory for data analysis during the last two decades. CKDD aims to support a humancentered process of discovering knowledge from data by visualizing and analyzing the conceptual structure of the data. We dicuss how the management system TOSCANA for conceptual information systems supports CKDD, and illustrate it by two applications in database marketing and flight movement analysis. Finally, we present a new tool for conceptual deviation discovery, Chianti. 1
Generic association rule bases: Are they so succinct?
 Proceedings of the CLA conference
, 2006
"... Abstract. In knowledge mining, current trend is witnessing the emergence of a growing number of works towards defining "concise and lossless" representations. One main motivation behind is: tagging a unified framework for drastically reducing large sized sets of association rules. In this ..."
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Abstract. In knowledge mining, current trend is witnessing the emergence of a growing number of works towards defining "concise and lossless" representations. One main motivation behind is: tagging a unified framework for drastically reducing large sized sets of association rules. In this context, generic bases of association rules whose backbone is the conjunction of the concepts of minimal generator and closed itemset (CI) constituted so far irreducible compact nuclei of association rules. However, the inherent absence of a unique minimal generator (MG) associated to a given CI offers an "ideal" gap towards a tougher redundancy removal even from generic bases of association rules. In this paper, we adopt the succinct system of minimal generators (SSMG), newly redefined in [1], to be an exact representation of the MG set. Then, we incorporate the SSMG into the framework of generic bases to only maintain the succinct generic association rules. After that, we give a thorough formal study of the related inference mechanisms allowing to derive all redundant association rules starting from succinct ones. Finally, an experimental study shows that our approach makes it possible to eliminate without information loss an important number of redundant generic association rules and thus, to only present succinct and informative ones to users.