Název: Robust Knowledge Discovery from High-Dimensional Data
Autoři: Kalina, Jan
Typ dokumentu: Příspěvky z konference
Konference/Akce: International Days of Statistics and Economics /6./, Prague (CZ), 2012-09-13 / 2012-09-15
Rok: 2012
Jazyk: eng
Abstrakt: The paper is devoted to advanced robust methods for information extraction from highdimensional data. The concept of knowledge discovery is discussed together with its two important aspects: high dimensionality of the data and sensitivity to the presence of outlying data values. We propose new robust methods for knowledge discovery suitable for highdimensional data. They are based on the idea of implicit weighting, which is inspired by the least weighted squares regression estimator. We propose a highly robust method for a dimension reduction, which can be described as a robust alternative of the principal component analysis based on implicit down-weighting of less reliable data values. Further, we propose a novel robust approach to cluster analysis, which is a popular knowledge discovery method of unsupervised learning. A two-stage cluster analysis method tailor-made for highdimensional data is obtained by combining the robust principal component analysis with the robust cluster analysis. The procedure can be interpreted as a robust knowledge discovery method tailor made for high-dimensional data.
Klíčová slova: cluster analysis; dimension reduction; principal components; robust statistics
Zdrojový dokument: International collection of scientific work on the occasion of 60th anniversary of university education at faculty of Business Economy with seat in Košice of University of Economics in Bratislava, ISBN 978-80-86175-80-5

Instituce: Ústav informatiky AV ČR (web)
Informace o dostupnosti dokumentu: Dokument je dostupný v repozitáři Akademie věd.
Původní záznam: http://hdl.handle.net/11104/0218503

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-151631


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