Clustering for Classification: Using Standard Clustering Methods - Tapa blanda

Evans, Reuben

 
9783639031638: Clustering for Classification: Using Standard Clustering Methods
Tapa blanda
ISBN 10: 3639031636 ISBN 13: 9783639031638
Editorial: VDM Verlag Dr. Mueller e.K., 2008

Sinopsis

Advances in technology have provided industry with an array of de­vices for collecting data. The frequency and scale of data collection means that there are now many large datasets being generated. To find patterns in these datasets it would be useful to be able to apply modern methods of classification such as support vector machines. Unfortunately these methods are computationally expensive, quadra­tic in the number of data points in fact, and so cannot be applied directly.This book proposes a framework whereby a variety of clustering methods can be used to summarise datasets, that is, reduce them to a smaller but still representative dataset so that these advanced me­thods can be applied. It compares the results of using this framework against using random selection on a large number of classification and regression problems. Results show that the clustered datasets are on average fifty percent smaller than the original datasets without loss of classification accuracy which is significantly better than ran­dom selection. They also show that there is no free lunch, for each dataset it is important to choose a clustering method carefully.

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

Advances in technology have provided industry with an array of de­vices for collecting data. The frequency and scale of data collection means that there are now many large datasets being generated. To find patterns in these datasets it would be useful to be able to apply modern methods of classification such as support vector machines. Unfortunately these methods are computationally expensive, quadra­tic in the number of data points in fact, and so cannot be applied directly. This book proposes a framework whereby a variety of clustering methods can be used to summarise datasets, that is, reduce them to a smaller but still representative dataset so that these advanced me­thods can be applied. It compares the results of using this framework against using random selection on a large number of classification and regression problems. Results show that the clustered datasets are on average fifty percent smaller than the original datasets without loss of classification accuracy which is significantly better than ran­dom selection. They also show that there is no free lunch, for each dataset it is important to choose a clustering method carefully.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
VDM Verlag Dr. Mueller e.K.
Año de publicación
2008
Idioma
Inglés
ISBN 10 
3639031636
ISBN 13 
9783639031638
Encuadernación
Tapa blanda
Número de páginas
108
Contacto del fabricante
no disponible
Persona responsable
no disponible