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Adaptive fuzzy partition in database mining: application to olfaction

Authors:

M Pintore,

Laboratory of Chemometrics & BioInformatics, University of Orléans, BP 6759, 45067 Orléans Cedex2, France, FR
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K Audouze,

Laboratory of Chemometrics & BioInformatics, University of Orléans, BP 6759, 45067 Orléans Cedex2, France., FR
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F Ros,

Laboratory of Chemometrics & BioInformatics, University of Orléans, BP 6759, 45067 Orléans Cedex2, France., FR
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J R Chrétien

Laboratory of Chemometrics & BioInformatics, University of Orléans, BP 6759, 45067 Orléans Cedex2, France, FR
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Abstract

A data set of 412 olfactory compounds, divided into animal, camphoraceous, ethereal and fatty olfaction classes, was submitted to an analysis by a Fuzzy Logic procedure called Adaptive Fuzzy Partition (AFP).This method aims to establish molecular descriptor/chemical activity relationships by dynamically dividing the descriptor space into a set of fuzzily partitioned subspaces. The ability of these AFP models to classify the four olfactory notes was validated after dividing the data set compounds into training and test sets, including 310 and 102 molecules, respectively. The main olfactory note was correctly predicted for 83 % of the test set compounds.
DOI: http://doi.org/10.2481/dsj.1.99
How to Cite: Pintore, M. et al., (2006). Adaptive fuzzy partition in database mining: application to olfaction. Data Science Journal. 1(1), pp.99–110. DOI: http://doi.org/10.2481/dsj.1.99
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Published on 05 Jan 2006.
Peer Reviewed

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