Dimensionality Reduction For Small, medium And Large Scale Data : A Quick Reference

Subramanian alias Balamurugan Appavu

ISBN 10: 3659231711 / ISBN 13: 9783659231711

Edité par LAP Lambert Academic Publishing, 2012

A propos de cet article

nach der Bestellung gedruckt Neuware - Printed after ordering - At the highest level of description, this book is about dimensionality reduction for data mining. It focuses on dimensional reduction on Small, Medium and very large Scale data. This book is not intended as an introduction to Feature selection, although we do provide the background necessary in several areas in order to facilitate the reader s comprehension of their respective roles in data mining. This book reviews state-of-the-art dimensionality reduction techniques for analyzing enormous quantities of raw data in high-dimensional data spaces to extract new information useful to the decision-making process.

N° de réf. du vendeur 9783659231711

Détails bibliographiques

• Titre: Dimensionality Reduction For Small, medium And Large Scale Data : A Quick Reference
• Éditeur: LAP Lambert Academic Publishing
• Date de parution: 2012
• Langue: anglais
• ISBN à 10 chiffres: 3659231711
• ISBN à 13 chiffres: 9783659231711
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 22 L x 15 H x 0,7 P (centimètres) 22 centimètres de largeur par 15 centimètres de hauteur par 0,7 centimètres de profondeur
• Poids: 201 grammes

A propos de ce titre

Synopsis

At the highest level of description, this book is about dimensionality reduction for data mining. It focuses on dimensional reduction on Small, Medium and very large Scale data. This book is not intended as an introduction to Feature selection, although we do provide the background necessary in several areas in order to facilitate the reader’s comprehension of their respective roles in data mining. This book reviews state-of-the-art dimensionality reduction techniques for analyzing enormous quantities of raw data in high-dimensional data spaces to extract new information useful to the decision-making process.