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Advances in Knowledge Discovery and Management: Volume 7 Softcover reprint of the original 1st ed. 2018 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 147 pages, height x width: 235x155 mm, weight: 454 g, 35 Illustrations, color; 11 Illustrations, black and white; XIII, 147 p. 46 illus., 35 illus. in color., 1 Paperback / softback
  • Sērija : Studies in Computational Intelligence 732
  • Izdošanas datums: 15-Aug-2018
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319880209
  • ISBN-13: 9783319880204
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Advances in Knowledge Discovery and Management: Volume 7 Softcover reprint of the original 1st ed. 2018Palielināt
  • Formāts: Paperback / softback, 147 pages, height x width: 235x155 mm, weight: 454 g, 35 Illustrations, color; 11 Illustrations, black and white; XIII, 147 p. 46 illus., 35 illus. in color., 1 Paperback / softback
  • Sērija : Studies in Computational Intelligence 732
  • Izdošanas datums: 15-Aug-2018
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319880209
  • ISBN-13: 9783319880204
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783319880204.html

This book is a collection of representative and novel works in the field of data mining, knowledge discovery, clustering and classification. Discussing both theoretical and practical aspects of “Knowledge Discovery and Management” (KDM), it is intended for researchers interested in these fields, including PhD and MSc students, and researchers from public or private laboratories.

The contributions included are extended and reworked versions of six of the best papers that were originally presented in French at the EGC’2016 conference held in Reims (France) in January 2016. This was the 16th edition of this successful conference, which takes place each year, and also featured workshops and other events with the aim of promoting exchanges between researchers and companies concerned with KDM and its applications in business, administration, industry and public organizations. For more details about the EGC society, please consult egc.asso.fr.

A Combined Approach for Ontology Enrichment from Textual and Open Data.- C-SPARQL Extension for Sampling RDF Graphs Streams.- Ef?ciency Analysis of ASP Encodings for Sequential Pattern Mining Tasks.