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Rough set approach to reasoning under uncertainty is based on inducing knowledge representation from data under constraints expressed by discernibility or, more generally, similarity of objects. Knowledge derived by this approach consists of reducts, decision or association rules, dependencies, templates, or classifiers. This monograph presents the state of the art of this area. The reader will find here a deep theoretical discussion of relevant notions and ideas as well as rich inventory of algorithmic and heuristic tools for knowledge discovery by rough set methods. An extensive bibliography will help the reader to get an acquaintance with this rapidly growing area of research.
Intelligent decision support is based on human knowledge related to a specific part of a real or abstract world. When the knowledge is gained by experience, it is induced from empirical data. The data structure, called an information system, is a record of objects described by a set of attributes. Knowledge is understood here as an ability to classify objects. Objects being in the same class are indiscernible by means of attributes and form elementary building blocks (granules, atoms). In particular, the granularity of knowledge causes that some notions cannot be expressed precisely within available knowledge and can be defined only vaguely. In the rough sets theory created by Z. Pawlak each...
Of Testing ExperimentsConclusion; Acknowledgments; References; Can Relational Learning Scale Up?; Introduction; Phase Transition in Hypothesis Testing; Experiment Goal and Setting; Results; Interpretation; The Phase Transition Is an Attractor; Correct Identification of the Target Concept; Good Approximation of the Target Concept; Conclusion; References; Discovering Geographic Knowledge: The INGENS System; Introduction; INGENS Software Architecture and Object Data Model; Learning Classification Rules for Geographical Objects; Application to Apulian Map Interpretation.
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Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.
Widely considered the premier text in pediatric infectious diseases, Feigin and Cherry's Textbook of Pediatric Infectious Diseases, 9th Edition, provides authoritative, up-to-date coverage of this rapidly changing field. Extensively revised by Drs. James Cherry, Sheldon L. Kaplan, Gail J. Demmler-Harrison, William J. Steinbach, Peter J. Hotez, and new editor John V. Williams, this two-volume reference delivers the information you need on epidemiology, public health, preventive medicine, clinical manifestations, diagnosis, treatment, and much more. It serves as a reliable, everyday resource for practicing ID specialists, and an invaluable reference for medical students, residents, and fellows...