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Description du livre Etat : New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. N° de réf. du vendeur ria9783843362122_lsuk
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Description du livre Etat : New. N° de réf. du vendeur ABLING22Oct2817100612108
Description du livre Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Clustering is a widely used knowledge discovery technique. Large-scale clustering has received a lot of attention recently. However, existing algorithms often do not scale with the size of the data and the number of dimensions, or fail to find arbitrary shapes of clusters or deal effectively with the presence of noise. In this book a new clustering algorithm based on self-similarity properties is discussed. Self-similarity is the property of being invariant with respect to the scale used to look at the data set. While fractals are self-similar at every scale, many data sets only exhibit self-similarity over a range of scales. Self- similarity can be measured using the fractal dimension. Our new clustering algorithm called Fractal Clustering (FC) places points incrementally in the cluster for which the change in the fractal dimension after adding the point is the least, so points in the same cluster have a great degree of self-similarity among them (and much less self- similarity with respect to points in other clusters). Two applications on projected clustering and tracking deviation in evolving data sets are also discussed. 140 pp. Englisch. N° de réf. du vendeur 9783843362122
Description du livre PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N° de réf. du vendeur L0-9783843362122
Description du livre Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Clustering is a widely used knowledge discovery technique. Large-scale clustering has received a lot of attention recently. However, existing algorithms often do not scale with the size of the data and the number of dimensions, or fail to find arbitrary shapes of clusters or deal effectively with the presence of noise. In this book a new clustering algorithm based on self-similarity properties is discussed. Self-similarity is the property of being invariant with respect to the scale used to look at the data set. While fractals are self-similar at every scale, many data sets only exhibit self-similarity over a range of scales. Self- similarity can be measured using the fractal dimension. Our new clustering algorithm called Fractal Clustering (FC) places points incrementally in the cluster for which the change in the fractal dimension after adding the point is the least, so points in the same cluster have a great degree of self-similarity among them (and much less self- similarity with respect to points in other clusters). Two applications on projected clustering and tracking deviation in evolving data sets are also discussed. N° de réf. du vendeur 9783843362122
Description du livre PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N° de réf. du vendeur L0-9783843362122
Description du livre Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chen PingDr. Ping Chen is an Associate Professor of Computer Science and the Director of Artificial Intelligence Lab at the University of Houston-Downtown. His research interests include Data Mining, and Computational Semantics. D. N° de réf. du vendeur 5466173