Honghua Dai, James N. K. Liu, Evgueni Smirnov, "Reliable Knowledge Discovery"
Publisher: S..in..r | ISBN: 1461419026 | 2012 | PDF | 326 pages | 7 MB
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.