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An Introduction to Symbolic Data Analysis and the Sodas Software
 Journal of Symbolic Data Analysis
, 2003
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An L1norm PCA and a heuristic approach
, 1996
"... : A model for a L 1 principal component analysis (PCA) is considered and discussed. On the other hand, a PCA based on Gini's mean absolute differences is introduced in order to obtain heuristic estimates of the L 1 model. This PCA is connected to the canonical correlation analysis of the original da ..."
Abstract

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: A model for a L 1 principal component analysis (PCA) is considered and discussed. On the other hand, a PCA based on Gini's mean absolute differences is introduced in order to obtain heuristic estimates of the L 1 model. This PCA is connected to the canonical correlation analysis of the original data and their ranks. The method is illustrated on real and artificial data with the aim of achieving proper data reduction. 1 Introduction Principal Component Analysis (PCA) is a widely used technique for dimension reduction of conventional data. For example, in pattern recognition, dimension reduction is an important stage as it often helps to reduce the cost of clustering and leads to the same classification results with a limited number of features instead of all. To increase the adaptability and flexibility of PCA, it is convenient to consider it as a combination of a model and of an estimation procedure under some probability distribution assumptions. Examples of this approach are the ...
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