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Titlebook: Advances in Knowledge Discovery and Management; Fabrice Guillet,Bruno Pinaud,Djamel Abdelkader Zig Book 2013 Springer-Verlag Berlin Heidel

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期刊全称Advances in Knowledge Discovery and Management
影响因子2023Fabrice Guillet,Bruno Pinaud,Djamel Abdelkader Zig
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发行地址Recent Research in Knowledge Discovery and Management.Carefully edited postproceedings book.Written by a leading expert in the field
学科分类Studies in Computational Intelligence
图书封面Titlebook: Advances in Knowledge Discovery and Management;  Fabrice Guillet,Bruno Pinaud,Djamel Abdelkader Zig Book 2013 Springer-Verlag Berlin Heidel
影响因子.The recent and novel research contributions collected in this book are extended and.reworked versions of a selection of the best papers that were originally presented in.French at the EGC’2011 Conference held in Brest, France, on January 2011..EGC stands for "Extraction et Gestion des connaissances" in French, and means "Knowledge Discovery and Management" or KDM..KDM is concerned with the works in computer science at the interface between data and knowledge; such as Data Mining, Knowledge Discovery, Business Intelligence, Knowledge Engineering and Semantic Web. .This book is intended to be read by all researchers interested in these fields, including.PhD or MSc students, and researchers from public or private laboratories. It.concerns both theoretical and practical aspects of KDM..This book has been structured in two parts. .The first part, entitled “Data Mining, classification and queries”, deals with rule and pattern mining, with topological approaches.and with OLAP..The second part of the book, entitled “Ontology and Semantic”, is related.to knowledge-based and user-centered approaches in KDM..
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A New Way for Hierarchical and Topological Clusteringta for wildland fuel science has never been synthesized into one reference; most studies were done for diverse and unique objectives.  This book is the first to link the disparate fields of ecology, wildland fi978-3-319-35254-1978-3-319-09015-3
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Iris Bombe Around Intraocular Lens Implantbeled data sets. Our criterion arises from a Bayesian approach: we propose an expression of the probability of a rule given the data. The most probable rules are thus the rules that are robust. Our Bayesian criterion is derived from this defined expression and allows us to mark out the robust rules
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