Springer Inhibitory Rules in Data Analysis: A Rough Set Approach: 163 (Studies in Computational Intelligence, 163)

Product Description This monograph is devoted to theoretical and experimental study of inhibitory decision and association rules. The most important feature of this monograph is that it includes an advanced mathematical analysis of problems on inhibitory rules. Review From the reviews: "This monograph is devoted to the theoretical and experimental study of decision and association rules. The most interesting part of the book is that it discusses an advanced mathematical analysis of problems and its rules. … I am sure that this book will be very useful to researchers in the area of data mining and the analysis and design of concurrent systems. It will be useful for PhD students in their very first year of study." (Prabhat Kumar Mahanti, Zentralblatt MATH, Vol. 1157, 2009) From the Back Cover 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 does not equal value". The use of inhibitory rules instead of deterministic (standard) ones allows us to describe more completely information encoded in decision or information systems and to design classifiers of high quality. The most important feature of this monograph is that it includes an advanced 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 classifiers 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.