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An Introduction to Symbolic Data Analysis and the Sodas Software
 Journal of Symbolic Data Analysis
, 2003
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Extracting Formal Concepts out of Relational Data
 Proceedings of the 4th Intl. Conference Journées de l’Informatique Messine (JIM’03): Knowledge Discovery and Discrete Mathematics, Metz (FR), 36 September
, 2003
"... Relational datasets, i.e., datasets in which individuals are described both by their own features and by their relations to other individuals, arise from various sources such as databases, both relational and objectoriented, or software models, e.g., UML class diagrams. When processing such complex ..."
Abstract

Cited by 5 (4 self)
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Relational datasets, i.e., datasets in which individuals are described both by their own features and by their relations to other individuals, arise from various sources such as databases, both relational and objectoriented, or software models, e.g., UML class diagrams. When processing such complex datasets, it is of prime importance for an analysis tool to hold as much as possible to the initial format so that the semantics is preserved and the interpretation of the final results eased. There have been several attempts to introduce relations into the Galois lattice and formal concept analysis fields. We propose a novel approach to this problem which relies on an enhanced version of the classical binary data descriptions based on the distinction of several mutually related formal contexts. Key Words: Galois lattices, conceptual scaling, lattice constructing algorithms, relational information. 1
Similaritybased Clustering versus Galois lattice building: Strengths and Weaknesses
 Workshop ’Objects and Classification, A Natural Convergence’. European Conference on ObjectOriented Programming
, 2000
"... In many realworld applications, designers tend towards building classes of objects such as concepts, chunks and clusters according to some similarity criteria. In this paper, we first compare two approaches to clustering: the Galois lattice approach [14] and a similaritybased clustering approac ..."
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Cited by 3 (0 self)
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In many realworld applications, designers tend towards building classes of objects such as concepts, chunks and clusters according to some similarity criteria. In this paper, we first compare two approaches to clustering: the Galois lattice approach [14] and a similaritybased clustering approach [27]. Then, we sketch the possible ways each approach can benefit from the other in refining the process of building a hierarchy of classes out of a set of instances.
A fast and general algorithm for Galois lattices building
 J. of Symbolic Data Analysis
, 2005
"... Standard Galois Lattices are effective tools for data analysis and knowledge discovery. They allow structuring data sets, by extracting concepts and rules to deduce concepts from other concepts. They focus on binary data arrays, called contexts. Several algorithms were proposed to generate concepts ..."
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Cited by 2 (0 self)
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Standard Galois Lattices are effective tools for data analysis and knowledge discovery. They allow structuring data sets, by extracting concepts and rules to deduce concepts from other concepts. They focus on binary data arrays, called contexts. Several algorithms were proposed to generate concepts or concept lattices on a data context. However, the mining of large databases needs more efficient algorithms. Nowadays, we need to deal with contexts which are large and not necessarily binary. In this paper, we propose a new and fast Galois latticebuilding algorithm, called ELL algorithm, for generating closed itemsets from objects having general descriptions; and a generalization of the Ganter algorithm (GGA). A comparison of performance between GGA, ELL, and another published algorithm, called Close By One, is presented. 1
Knowledge Discovery From Symbolic Data And The Sodas Software
 Conf. on Principles and Practice of Knowledge Discovery in Databases, PPKDD2000
, 2000
"... The data descriptions of the units are called "symbolic" when they are more complex than the standard ones due to the fact that they contain internal variation and are structured. Symbolic data happen from many sources, for instance in order to summarise huge Relational Data Bases by their under ..."
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The data descriptions of the units are called "symbolic" when they are more complex than the standard ones due to the fact that they contain internal variation and are structured. Symbolic data happen from many sources, for instance in order to summarise huge Relational Data Bases by their underlying concepts. "Extracting knowledge" means getting explanatory results, that why, "symbolic objects" are introduced and studied in this paper. They model concepts and constitute an explanatory output for data analysis. Moreover they can be used in order to define queries of a Relational Data Base and propagate concepts between Data Bases. We define "Symbolic Data Analysis" (SDA) as the extension of standard Data Analysis to symbolic data tables as input in order to find symbolic objects as output. In this paper we give an overview on recent development on SDA. We present some tools and methods of SDA and introduce the SODAS software prototype (issued from the work of 17 teams of nine countries involved in an European project of EUROSTAT). 1
STRUCTURING PROBABILISTIC DATA BY GALOIS LATTICES
 MATH. & SCI. HUM. / MATHEMATICS AND SOCIAL SCIENCES (43 E ANNÉE, N ° 169, 2005(1), P. 77104)
, 2005
"... In this paper we address the problem of organising probabilistic data by Galois concept lattices. Two lattices are proposed, the union lattice and the intersection lattice, corresponding to two distinct semantics, by choosing accordingly the join and meet operators. A new algorithm is proposed to c ..."
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Cited by 1 (1 self)
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In this paper we address the problem of organising probabilistic data by Galois concept lattices. Two lattices are proposed, the union lattice and the intersection lattice, corresponding to two distinct semantics, by choosing accordingly the join and meet operators. A new algorithm is proposed to construct the concept lattice. Two real data examples illustrate the presented approach.
A Adistributed version version of the of Ganter the Ganter algorithm algorithm for general for general Galois Galois Lattices Lattices
"... Abstract. Standard Galois Lattices are effective tools for data analysis and knowledge discovery. Several algorithms were proposed to generate concepts of lattices, among which the ScalingNextClosure algorithm. In order to share the production workload between several processors when the number of c ..."
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Abstract. Standard Galois Lattices are effective tools for data analysis and knowledge discovery. Several algorithms were proposed to generate concepts of lattices, among which the ScalingNextClosure algorithm. In order to share the production workload between several processors when the number of closed itemsets to determine is very large, this algorithm leans on the sequential character of the closed itemsets determination of a Galois Lattice by the Ganter algorithm. In this paper, we prove that the parallelised version of the ScalingNextClosure can be extended to more general contexts (even some complex data) than usual binary contexts and that the partition of the workload between processors can be made with all the wished precision. 1
A fast and general algorithm to build Galois lattices
"... Standard Galois Lattices are effective tools for data analysis and knowledge discovery. Many research works in classification or association rules increase the interest of them for data mining. They allow structuring data sets, by extracting concepts and rules to deduce concepts from other concepts. ..."
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Standard Galois Lattices are effective tools for data analysis and knowledge discovery. Many research works in classification or association rules increase the interest of them for data mining. They allow structuring data sets, by extracting concepts and rules to deduce concepts from other concepts. They concern binary data arrays, called contexts. Several algorithms were proposed to generate concepts of lattices, among which Ganter algorithm is the best one for large contexts. However, it’s also hard to face the complexity of large data with Ganter algorithm. So we propose a new and fast Galois latticebuilding algorithm GL which works for objects having very general descriptions. And we propose a way to parallelise large context.