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Titlebook: Advances in Knowledge Discovery and Management; Volume 2 Fabrice Guillet,Gilbert Ritschard,Djamel Abdelkade Book 2012 Springer-Verlag Berli

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期刊全稱Advances in Knowledge Discovery and Management
期刊簡稱Volume 2
影響因子2023Fabrice Guillet,Gilbert Ritschard,Djamel Abdelkade
視頻videohttp://file.papertrans.cn/149/148662/148662.mp4
發(fā)行地址Recent research in Computational Intelligence.Advances in Knowledge Discovery and Management.Written by leading experts in the field
學(xué)科分類Studies in Computational Intelligence
圖書封面Titlebook: Advances in Knowledge Discovery and Management; Volume 2 Fabrice Guillet,Gilbert Ritschard,Djamel Abdelkade Book 2012 Springer-Verlag Berli
影響因子.During the last decade, Knowledge Discovery and Management (KDM or, in French, EGC for Extraction et Gestion des connaissances) has been an intensive and fruitful research topic in the French-speaking scientific community. In 2003, this enthusiasm for KDM led to the foundation of a specific French-speaking association, called EGC, dedicated to supporting and promoting this topic. More precisely, KDM is concerned with the interface between knowledge and data such as, among other things, Data Mining, Knowledge Discovery, Business Intelligence, Knowledge Engineering and Semantic Web. 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 2010 Conference held in Tunis, Tunisia in January 2010. .The volume is organized in three parts. Part I includes four chapters concerned with various aspects of Data Cube and Ontology-based representations. Part II is composed of four chapters concerned with Efficient Pattern Mining issues, while in Part III the last four chapters address Data Preprocessing and Information Retrieval..
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978-3-642-42779-4Springer-Verlag Berlin Heidelberg 2012
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Retinoblastoma: Evolving Therapies,aggregated data is huge and requires large amounts of storage space and mining time. To address this issue, various research works highlighted the added-value of compact representations of data cubes from which the remaining redundant patterns can be derived. In this respect, we introduce in this ch
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Eyelid Tumors: Examination Techniques,onal density estimation, filter selection of input variables, variable recoding for classification methods. These methods usually assume a small number of classes, typically less than ten, and reach their limit in case of too many classes. In this paper, we extend discretization and value grouping m
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