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Inhibitory Rules in Data Analysis - A Rough Set Approach (Hardcover, 2009 ed.)
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Inhibitory Rules in Data Analysis - A Rough Set Approach (Hardcover, 2009 ed.)
Series: Studies in Computational Intelligence, 163
Expected to ship within 10 - 15 working days
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This monograph is devoted to theoretical and experimental study of
inhibitory decision and association rules. Inhibitory rules contain
on the right-hand side a relation of the kind "attribut = value."
The use of inhibitory rules instead of deterministic (standard)
ones allows us to describe more completely infor- tion encoded in
decision or information systems and to design classi?ers of high
quality. The mostimportantfeatureofthis monographis thatit
includesanadvanced mathematical analysis of problems on inhibitory
rules. We consider algorithms for construction of inhibitory rules,
bounds on minimal complexity of inhibitory rules, and algorithms
for construction of the set of all minimal inhibitory rules. We
also discuss results of experiments with standard and lazy
classi?ers based on inhibitory rules. These results show that
inhibitory decision and association rules can be used in data
mining and knowledge discovery both for knowledge representation
and for prediction. Inhibitory rules can be also used under the
analysis and design of concurrent systems. The results obtained in
the monograph can be useful for researchers in such areas as
machine learning, data mining and knowledge discovery, especially
for those who are working in rough set theory, test theory, and
logical analysis of data (LAD). The monograph can be used under the
creation of courses for graduate students and for Ph.D. studies.
TheauthorsofthisbookextendanexpressionofgratitudetoProfessorJanusz
Kacprzyk, to Dr. Thomas Ditzinger and to the Studies in
Computational Int- ligence sta? at Springer for their support in
making this book possible.
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