Machine Learning and Data Mining in Pattern Recognition [electronic resource] : 14th International Conference, MLDM 2018, New York, NY, USA, July 15-19, 2018, Proceedings, Part I /

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Material type: Text

TextSeries: Lecture Notes in Artificial Intelligence ; 10934Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018Description: XV, 455 p. 261 illus., 112 illus. in color. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783319961361

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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 -- Image mining -- Text mining -- Video mining.-Web mining -- Graph mining -- Process mining.

In: Springer Nature eBookSummary: This two-volume set LNAI 10934 and LNAI 10935 constitutes the refereed proceedings of the 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018, held in New York, NY, USA in July 2018. The 92 regular papers presented in this two-volume set were carefully reviewed and selected from 298 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.

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Classification -- Clustering -- Image mining -- Text mining -- Video mining.-Web mining -- Graph mining -- Process mining.

This two-volume set LNAI 10934 and LNAI 10935 constitutes the refereed proceedings of the 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018, held in New York, NY, USA in July 2018. The 92 regular papers presented in this two-volume set were carefully reviewed and selected from 298 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.

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