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Scalable Non-Parametric Pattern Recognition Techniques for Data Mining: Fast Non-Parametric Classification and Clustering Methods for Large Data Sets - Softcover

 
9783845416205: Scalable Non-Parametric Pattern Recognition Techniques for Data Mining: Fast Non-Parametric Classification and Clustering Methods for Large Data Sets

Inhaltsangabe

Knowledge discovery in data is called data mining. Many data mining techniques require classification and clustering. Large data sets are available nowadays in the world and fast approaches of classification or clustering becomes a tedious work with large data sets. For example, computer vision, text mining, semantic web mining, natural language processing etc., require non-parametric pattern recognition methods. This book describes fast approaches to discover knowledge from large data sets.This book deals with condensing of large data and also preserving essential information in the data. This book describes many efficient fast classifiers and clustering methods which are based on density information in the large data sets. It describes to resolve vagueness and uncertainty that is present in large data sets using combination principles of rough sets and fuzzy sets. Approaches in this book are adaptive and they can be applied in many machine learning methods in many domains.

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Reseña del editor

Knowledge discovery in data is called data mining. Many data mining techniques require classification and clustering. Large data sets are available nowadays in the world and fast approaches of classification or clustering becomes a tedious work with large data sets. For example, computer vision, text mining, semantic web mining, natural language processing etc., require non-parametric pattern recognition methods. This book describes fast approaches to discover knowledge from large data sets.This book deals with condensing of large data and also preserving essential information in the data. This book describes many efficient fast classifiers and clustering methods which are based on density information in the large data sets. It describes to resolve vagueness and uncertainty that is present in large data sets using combination principles of rough sets and fuzzy sets. Approaches in this book are adaptive and they can be applied in many machine learning methods in many domains.

Biografía del autor

Dr. Suresh Veluru is a postdoctoral fellow at University of New Brunswick,Canada.He received his PhD from Indian Institute of Technology Guwahati, India in 2009. Dr. P. Viswanath is a Professor and Dean R&D (Electrical Sciences) at Rajeev Gandhi Memorial College of Eng. & Tech., Nandyal. He received his PhD from IISc, Bangalore,India.

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ISBN 10: 3845416203 ISBN 13: 9783845416205
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Taschenbuch. Zustand: Neu. Neuware -Knowledge discovery in data is called data mining. Many data mining techniques require classification and clustering. Large data sets are available nowadays in the world and fast approaches of classification or clustering becomes a tedious work with large data sets. For example, computer vision, text mining, semantic web mining, natural language processing etc., require non-parametric pattern recognition methods. This book describes fast approaches to discover knowledge from large data sets.This book deals with condensing of large data and also preserving essential information in the data. This book describes many efficient fast classifiers and clustering methods which are based on density information in the large data sets. It describes to resolve vagueness and uncertainty that is present in large data sets using combination principles of rough sets and fuzzy sets. Approaches in this book are adaptive and they can be applied in many machine learning methods in many domains.Books on Demand GmbH, Überseering 33, 22297 Hamburg 116 pp. Englisch. Artikel-Nr. 9783845416205

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Veluru, Suresh; Pulabaigari, Viswanath; Veluru, Suresh; Pulabaigari, Viswanath
ISBN 10: 3845416203 ISBN 13: 9783845416205
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Paperback. Zustand: Brand New. 116 pages. 8.66x5.91x0.27 inches. In Stock. Artikel-Nr. 3845416203

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