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With the rapid development of the data mining and knowledge discovery, a key
issue which could significantly affect the real world applications of data mining is
the reliability issues of knowledge discovery. It is natural that people will ask if the
discovered knowledge is reliable. Why do we trust the discovered knowledge? How
much can we trust the discovered knowledge? When it could go wrong. All these
questions are very essential to data mining. It is especial crucial to the real world
applications.
One of the essential requirements of data mining is validity. This means both the
discovery process itself and the discovered knowledge should be valid. Reliability
is a necessary but not sufficient condition for validity. Reliability could be viewed
as stability, equivalence and consistency in some ways.
This special volume of the book on the reliability issues of Data Mining and
Knowledge Discovery will focus on the theory and techniques that can ensure the
discovered knowledge is reliable and to identify under which conditions the discovered
knowledge is reliable or in which cases the discovery process is robust. In the
last 20 years, many data mining algorithms have been developed for the discovery
of knowledge from given data bases. However in some cases, the discovery process
is not robust or the discovered knowledge is not reliable or even incorrect in certain
cases. We could also find that in some cases, the discovered knowledge may
not necessary be the real reflection of the data. Why does this happen? What are
the major factors that affect the discovery process? How can we make sure that the
discovered knowledge is reliable? What are the conditions under which a reliable
discovery can be assured? These are some interesting questions to be investigated
in this book.
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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