Dynamic Graph Learning for Dimension Reduction and Data Clustering
Series: Synthesis Lectures on Computer Science;
- Publisher's listprice EUR 42.79
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18 151 Ft (17 287 Ft + 5% VAT)
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18 151 Ft
Availability
Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
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Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 2024
- Publisher Springer
- Date of Publication 23 September 2024
- Number of Volumes 1 pieces, Book
- ISBN 9783031423154
- Binding Paperback
- No. of pages143 pages
- Size 240x168 mm
- Language English
- Illustrations 1 Illustrations, black & white; 40 Illustrations, color 648
Categories
Short description:
This book illustrates how to achieve effective dimension reduction and data clustering. The authors explain how to accomplish this by utilizing the advanced dynamic graph learning technique in the era of big data. The book begins by providing background on dynamic graph learning. The authors discuss why it has attracted considerable research attention in recent years and has become well recognized as an advanced technique. After covering the key topics related to dynamic graph learning, the book discusses the recent advancements in the area. The authors then explain how these techniques can be practically applied for several purposes, including feature selection, feature projection, and data clustering.
MoreLong description:
This book illustrates how to achieve effective dimension reduction and data clustering. The authors explain how to accomplish this by utilizing the advanced dynamic graph learning technique in the era of big data. The book begins by providing background on dynamic graph learning. The authors discuss why it has attracted considerable research attention in recent years and has become well recognized as an advanced technique. After covering the key topics related to dynamic graph learning, the book discusses the recent advancements in the area. The authors then explain how these techniques can be practically applied for several purposes, including feature selection, feature projection, and data clustering.
MoreTable of Contents:
Introduction.- Dynamic Graph Learning for Feature Projection.- Dynamic Graph Learning for Feature Selection.- Dynamic Graph Learning for Data Clustering.- Research Frontiers.
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