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An Introduction to Symbolic Data Analysis and the Sodas Software
 Journal of Symbolic Data Analysis
, 2003
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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
Pyramidal Clustering Algorithms in ISO3D Project
, 2000
"... Pyramidal clustering method generalizes hierarchies by allowing nondisjoint classes at a given level instead of a partition. Moreover, the clusters of the pyramid are intervals of a total order on the set being clustered. [Diday 1984], [Bertrand, Diday 1990] and [Mfoumoune 1998] proposed algorithms ..."
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Pyramidal clustering method generalizes hierarchies by allowing nondisjoint classes at a given level instead of a partition. Moreover, the clusters of the pyramid are intervals of a total order on the set being clustered. [Diday 1984], [Bertrand, Diday 1990] and [Mfoumoune 1998] proposed algorithms to build a pyramid starting with an arbitrary order of the individual. In this paper we present two new algorithms name CAPS and CAPSO. CAPSO builds a pyramid starting with an order given on the set of the individuals (or symbolic objects) while CAPS finds this order. These two algorithms allows moreover to cluster more complex data than the tabular model allows to process, by considering variation on the values taken by the variables, in this way, our method produces a symbolic pyramid. Each cluster thus formed is defined not only by the set of its elements (i.e. its extent) but also by a symbolic object, which describes its properties (i.e. its intent). These two algorithms were implemented in C++ and Java to the ISO3D project. 1 Definitions Diday in [5, Diday (1984)] proposes the algorithm CAP to build numeric pyramids. Algorithms are also presented with this purpose in [2, Bertrand y Diday (1990)], [10, Gil (1998)] and [11, Mfoumoune (1998)]. Paula Brito in [3, Brito (1991)] proposes a macroalgorithm that generalizes the algorithm to build numeric pyramids proposed by Bertrand to the symbolic case. In this article we propose two algorithm designed to build symbolic pyramids (CAPS and CAPSO), that is to say, a pyramid in which each node is again a symbolic object. These algorithms also calculate the extension of each one of these symbolic objects and verifie its completeness. Notation:  # the set of individuals.  O j the description space for the variable j....
Correspondences between Prepyramids, Pyramids and Robinsonian Dissimilarities
, 2012
"... We consider cluster structures in a general setting where they do not necessarily contain all singletons of the ground set. Then we provide a direct proof of the bijection between semiproper robinsonian dissimilarities and indexed prepyramids. This result generalizes its analogue proven by Batbeda ..."
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We consider cluster structures in a general setting where they do not necessarily contain all singletons of the ground set. Then we provide a direct proof of the bijection between semiproper robinsonian dissimilarities and indexed prepyramids. This result generalizes its analogue proven by Batbedat in the particular case of definite cluster structures. Moreover, the proposed proof shows that the clusters of the indexed prepyramid corresponding to a semiproper robinsonian dissimilarity are particular 2balls of the considered dissimilarity.
Aggregation Method to Reuse Knowledge from Project Memory
"... The knowledge engineering offers a rational framework allowing a representation of knowledge obtained through the experiences. This technique found a great application in knowledge management and especially to capitalize knowledge. In fact, the rational representation of knowledge allows their explo ..."
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The knowledge engineering offers a rational framework allowing a representation of knowledge obtained through the experiences. This technique found a great application in knowledge management and especially to capitalize knowledge. In fact, the rational representation of knowledge allows their exploitation and their reuse. It is a necessary condition to allow a reuse and a knowledge appropriation. The knowledge management must take into account this dimension, since its first concern is to make knowledge persistent, ready to be reused. In this paper, we study the traces classifications of the design project achievements in order to have a knowledge aggregation and to thus provide a representation of handled knowledge, directives and competences organization as well as negotiation strategies and cooperative problems solving.