Reliable Knowledge Discovery

Honghua Dai

ISBN 10: 1461419026 / ISBN 13: 9781461419020

Published by Springer, Springer Feb 2012, 2012

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -Reliable Knowledge Discovery focuses on theory, methods, and techniques for RKDD, a new sub-field of KDD. It studies the theory and methods to assure the reliability and trustworthiness of discovered knowledge and to maintain the stability and consistency of knowledge discovery processes. RKDD has a broad spectrum of applications, especially in critical domains like medicine, finance, and military.Reliable Knowledge Discovery also presents methods and techniques for designing robust knowledge-discovery processes. Approaches to assessing the reliability of the discovered knowledge are introduced. Particular attention is paid to methods for reliable feature selection, reliable graph discovery, reliable classification, and stream mining. Estimating the data trustworthiness is covered in this volume as well. Case studies are provided in many chapters.Reliable Knowledge Discovery is designed for researchers and advanced-level students focused on computer science and electrical engineering as a secondary text or reference. Professionals working in this related field and KDD application developers will also find this book useful.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 328 pp. Englisch.

Seller Inventory # 9781461419020

Bibliographic Details

• Title: Reliable Knowledge Discovery
• Publisher: Springer, Springer Feb 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 1461419026
• ISBN 13: 9781461419020
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 23D millimeters 241 millimeters width by 160 millimeters height by 23 millimeters depth
• Weight: 658 grams

About this title

Synopsis

Reliable Knowledge Discovery focuses on theory, methods, and techniques for RKDD, a new sub-field of KDD. It studies the theory and methods to assure the reliability and trustworthiness of discovered knowledge and to maintain the stability and consistency of knowledge discovery processes. RKDD has a broad spectrum of applications, especially in critical domains like medicine, finance, and military.

Reliable Knowledge Discovery also presents methods and techniques for designing robust knowledge-discovery processes. Approaches to assessing the reliability of the discovered knowledge are introduced. Particular attention is paid to methods for reliable feature selection, reliable graph discovery, reliable classification, and stream mining. Estimating the data trustworthiness is covered in this volume as well. Case studies are provided in many chapters.

Reliable Knowledge Discovery is designed for researchers and advanced-level students focused on computer science and electrical engineering as a secondary text or reference. Professionals working in this related field and KDD application developers will also find this book useful.