Symbolic data analysis

Known as: SDA 
Symbolic data analysis (SDA) is an extension of standard data analysis where symbolic data tables are used as input and symbolic objects are… (More)
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Papers overview

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2017
2017
Formal concept analysis (FCA) can be used for designing concept lattices from binary data for knowledge discovery purposes… (More)
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2013
2013
The Fuzzy Kohonen Clustering Network combines the idea of fuzzy membership values for learning rates. It is a kind of self… (More)
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Highly Cited
2012
Highly Cited
2012
Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. Recently… (More)
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2011
2011
This paper introduces symbolic data analysis, explaining how it extends the classical data models to take into account more… (More)
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2009
2009
Recommender systems have become an important tool to cope with the information overload problem by acquiring data about user… (More)
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Highly Cited
2008
Highly Cited
2008
Description: Classical statistical techniques are often inadequate when it comes to analysing some of the large and internally… (More)
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Highly Cited
2007
Highly Cited
2007
It's not surprisingly when entering this site to get the book. One of the popular books now is the symbolic data analysis… (More)
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Highly Cited
2002
Highly Cited
2002
Pattern discovery in unaligned DNA sequences is a fundamental problem in computational biology with important applications in… (More)
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2001
2001
Nowadays, data analysts are confronted with new challenges: they are asked to process data that go beyond the classical framework… (More)
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2000
2000
The present paper deals with the study of continuous interval data by means of suitable Principal Component Analyses (PCA… (More)
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