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Advances in Data Mining. Applications and Theoretical Aspects: 17th Industrial Conference, ICDM 2017, New York, NY, USA, July 12-13, 2017, Proceedings de Petra Perner

Advances in Data Mining. Applications and Theoretical Aspects: 17th Industrial Conference, ICDM 2017, New York, NY, USA, July 12-13, 2017, Proceedings: Lecture Notes in Computer Science, cartea 10357

Editat de Petra Perner
en Limba Engleză Paperback – 4 iul 2017
This book constitutes the refereed proceedings of the 17th Industrial Conference on Advances in Data Mining, ICDM 2017, held in New York, NY, USA, in July 2017. The 27 revised full papers presented were carefully reviewed and selected from 71 submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, in medicine, and in process control in industry and society.
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Specificații

ISBN-13: 9783319627007
ISBN-10: 3319627007
Pagini: 346
Ilustrații: XI, 346 p. 123 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.5 kg
Ediția:1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Cham, Switzerland

Cuprins

Theoretical aspects of data mining.-  Applications of data mining.- Multimedia data, in marketing, in medicine.- Process control in industry and society.

Caracteristici

Includes supplementary material: sn.pub/extras

Textul de pe ultima copertă

This book constitutes the refereed proceedings of the 12th Industrial Conference on Data Mining, ICDM 2012, held in Berlin, Germany in July 2012. The 22 revised full papers presented were carefully reviewed and selected from 97 submissions. The papers are organized in topical sections on data mining in medicine and biology; data mining for energy industry; data mining in traffic and logistic; data mining in telecommunication; data mining in engineering; theory in data mining; theory in data mining: clustering; theory in data mining: association rule mining and decision rule mining.