Machine Learning and Data Mining in Pattern Recognition [electronic resource] : 12th International Conference, MLDM 2016, New York, NY, USA, July 16-21, 2016, Proceedings /

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Material type: Text

TextSeries: Lecture Notes in Artificial Intelligence ; 9729Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Edition: 1st ed. 2016Description: XIII, 807 p. 291 illus. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783319419206

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Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:

006.3 23

LOC classification:

Q334-342
TA347.A78

Online resources:

Click here to access online

Contents:

Classification -- Clustering -- Association rule -- Pattern mining -- Image mining.-Text mining -- Video mining -- Web mining.

In: Springer Nature eBookSummary: This book constitutes the refereed proceedings of the 12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016, held in New York, NY, USA in July 2016. The 58 regular papers presented in this book were carefully reviewed and selected from 169 submissions. The topics range 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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Classification -- Clustering -- Association rule -- Pattern mining -- Image mining.-Text mining -- Video mining -- Web mining.

This book constitutes the refereed proceedings of the 12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016, held in New York, NY, USA in July 2016. The 58 regular papers presented in this book were carefully reviewed and selected from 169 submissions. The topics range 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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