Classification and Clustering (eBook, PDF)
Proceedings of an Advanced Seminar Conducted by the Mathematics Research Center, the University of Wisconsin at Madison, May 3-5, 1976
Redaktion: Ryzin, J. van
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Classification and Clustering documents the proceedings of the Advanced Seminar on Classification and Clustering held in Madison, Wisconsin on May 3-5, 1976.This compilation discusses the relationship between multidimensional scaling and clustering, distribution problems in clustering, and botryology of botryology. The graph theoretic techniques for cluster analysis algorithms, data dependent clustering techniques, and linguistic approach to pattern recognition are also elaborated. This text likewise covers the discriminant analysis when scale contamination is present in the initial sample and...
Classification and Clustering documents the proceedings of the Advanced Seminar on Classification and Clustering held in Madison, Wisconsin on May 3-5, 1976.
This compilation discusses the relationship between multidimensional scaling and clustering, distribution problems in clustering, and botryology of botryology. The graph theoretic techniques for cluster analysis algorithms, data dependent clustering techniques, and linguistic approach to pattern recognition are also elaborated. This text likewise covers the discriminant analysis when scale contamination is present in the initial sample and statistical basis of computerized diagnosis using the electrocardiogram. Other topics include the simple histogram method for nonparametric classification and optimal smoothing of density estimates.
This book is intended for mathematicians, biological scientists, social scientists, computer scientists, statisticians, and engineers interested in classification and clustering.
This compilation discusses the relationship between multidimensional scaling and clustering, distribution problems in clustering, and botryology of botryology. The graph theoretic techniques for cluster analysis algorithms, data dependent clustering techniques, and linguistic approach to pattern recognition are also elaborated. This text likewise covers the discriminant analysis when scale contamination is present in the initial sample and statistical basis of computerized diagnosis using the electrocardiogram. Other topics include the simple histogram method for nonparametric classification and optimal smoothing of density estimates.
This book is intended for mathematicians, biological scientists, social scientists, computer scientists, statisticians, and engineers interested in classification and clustering.
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