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Machine Learning and Data Mining in Pattern Recognition [electronic resource] : 9th International Conference, MLDM 2013, New York, NY, USA, July 19-25, 2013. Proceedings / edited by Petra Perner.

Contributor(s): Material type: TextTextSeries: Lecture Notes in Computer Science ; 7988Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XII, 660 p. 199 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783642397127
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 006.3 23
LOC classification:
  • Q334-342
  • TJ210.2-211.495
Online resources:
Contents:
Theoretical topics for classification -- Clustering -- Association rule and pattern mining.- Specific data mining methods for the different multimedia data types.- Image mining -- Text mining -- Video mining.-Web mining.
In: Springer eBooksSummary: This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2013, held in New York, USA in July 2013. The 51 revised full papers presented were carefully reviewed and selected from 212 submissions. The papers cover the topics ranging from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining.
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Theoretical topics for classification -- Clustering -- Association rule and pattern mining.- Specific data mining methods for the different multimedia data types.- Image mining -- Text mining -- Video mining.-Web mining.

This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2013, held in New York, USA in July 2013. The 51 revised full papers presented were carefully reviewed and selected from 212 submissions. The papers cover the topics ranging from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining.

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