"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.
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