Artificial neural network technology for the classification and cartography of scientific and technical information | Zendy

Xavier Polanco | Zendy; Claire François | Zendy; Jan Keim | Zendy

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Artificial neural network technology for the classification and cartography of scientific and technical information

Author(s) -

Xavier Polanco,

Claire François,

Jan Keim

Publication year - 1998

Publication title -

scientometrics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.999

H-Index - 116

eISSN - 1588-2861

pISSN - 0138-9130

DOI - 10.1007/bf02457968

Subject(s) - artificial neural network , self organizing map , principal component analysis , adaptive resonance theory , artificial intelligence , multilayer perceptron , computer science , pattern recognition (psychology) , associative property , cluster (spacecraft) , data mining , machine learning , mathematics , pure mathematics , programming language

This paper describes the implementation of multivariate data analysis: NEURODOC applies the axial k-means method for automatic, non-hierarchical cluster analysis and a Principal Component Analysis (PCA) for representing the clusters on a map. We next introduce Artificial Neural Networks (ANNs) to extend NEURODOC into a neural platform for the cluster analysis and cartography of bibliographic data. The ANNs tested are: the Adaptive Resonance Theory (ART 1), a Multilayer Perceptron (MLP), and an associative network with unsupervised learning (KOHONEN). This platform is intended for quantitative analysis of information.

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