The Industrial Conference on Data Mining ICDM-Leipzig was the sixth event in a series of annual events which started in 2000. ...
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Most data sets collected by researchers are multivariate, and in the majority of cases the variables need to be examined ...
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This book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians ...
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Presents advances in data analysis and decision support and gives an actual overview on the interface between mathematics, ...
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This book presents new developments in data analysis, classification and multivariate statistics, and in their algorithmic ...
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This book presents new developments in data analysis, classification and multivariate statistics, and in their algorithmic ...
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This volume provides new methodological developments in data analysis and classification. A wide range of topics is covered ...
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This volume provides new methodological developments in data analysis and classification. A wide range of topics is covered ...
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Delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various ...
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Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern ...
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This new volume in the series Springer Handbooks of Computational Statistics gives an overview of modern data visualization ...
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The book provides a comprehensive treatment of multidimensional scaling (MDS), a family of statistical techniques for analyzing ...
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Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the ...
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The first part is devoted to graphical techniques. The second part deals with multivariate random variables and presents ...
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This volume contains revised versions of selected papers presented during the biannual meeting of the Classification and ...
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This book describes existing and advanced methods to reduce the dimensionality of numerical databases. For each method, the ...
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In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools ...
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