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Advances in Knowledge Discovery and Management: Volume 4 2014 ed. [Hardback]

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  • Formāts: Hardback, 175 pages, height x width: 235x155 mm, weight: 4144 g, 25 Illustrations, color; 38 Illustrations, black and white; XVI, 175 p. 63 illus., 25 illus. in color., 1 Hardback
  • Sērija : Studies in Computational Intelligence 527
  • Izdošanas datums: 08-Nov-2013
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319029983
  • ISBN-13: 9783319029986
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  • Formāts: Hardback, 175 pages, height x width: 235x155 mm, weight: 4144 g, 25 Illustrations, color; 38 Illustrations, black and white; XVI, 175 p. 63 illus., 25 illus. in color., 1 Hardback
  • Sērija : Studies in Computational Intelligence 527
  • Izdošanas datums: 08-Nov-2013
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319029983
  • ISBN-13: 9783319029986
Citas grāmatas par šo tēmu:

This book is a collection of representative and novel works done in Data Mining, Knowledge Discovery, Clustering and Classification that were originally presented in French at the EGC'2012 Conference held in Bordeaux, France, on January 2012. This conference was the 12th edition of this event, which takes place each year and which is now successful and well-known in the French-speaking community. This community was structured in 2003 by the foundation of the French-speaking EGC society (EGC in French stands for ``Extraction et Gestion des Connaissances'' and means ``Knowledge Discovery and Management'', or KDM).

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. The book is structured in two parts called ``Knowledge Discovery and Data Mining'' and ``Classification and Feature Extraction or Selection''. The first part (6 chapters) deals with data clustering and data mining. The three remaining chapters of the second part are related to classification and feature extraction or feature selection.


Recenzijas

From the reviews:

All nine chapters of this volume are extensions or revisions of papers that were presented at the 12th Francophone International Conference on Knowledge Discovery and Management, held in Bordeaux, France, in 2012. The primary market for the book includes researchers in academia and in labs, whose primary focus includes knowledge discovery, data mining, classification, and feature extraction. (J. Hodges, Computing Reviews, February, 2014)

List of Contributors
xi
Part I Knowledge Discovery and Data Mining
Optimizing a Hierarchical Community Structure of a Complex Network
3(12)
Francois Queyroi
Nonparametric Hierarchical Clustering of Functional Data
15(22)
Marc Boulle
Romain Guigoures
Fabrice Rossi
Multi-view Clustering on Relational Data
37(16)
Francisco de A.T. de Carvalho
Yves Lechevallier
Thierry Despeyroux
Filipe M. de Melo
Relaxing Time Granularity for Mining Frequent Sequences
53(24)
Asma Ben Zakour
Sofian Maabout
Mohamed Mosbah
Marc Sistiaga
Semantic Event Extraction from Biological Texts Using a Kernel-Based Method
77(18)
Rim Faiz
Maha Amami
Aymen Elkhlifi
Supervised Pre-processing of Numerical Variables for Multi-Relational Data Mining
95(18)
Dhafer Lahbib
Marc Boulle
Dominique Laurent
Part II Classification and Feature Extraction or Selection
Combination of Single Feature Classifiers for Fast Feature Selection
113(20)
Hassan Chouaib
Florence Cloppet
Nicole Vincent
Classification of EEG Signals by an Evolutionary Algorithm
133(22)
Laurent Vezard
Pierrick Legrand
Marie Chavent
Frederique Faita-Ainseba
Julien Clauzel
Leonardo Trujillo
Large Scale Image Classification: Fast Feature Extraction, Multi-codebook Approach and Multi-core SVM Training
155(18)
Thanh-Nghi Doan
Francois Poulet
About the Editors 173(2)
Author Index 175