New Frontiers in Mining Complex Patterns [electronic resource] : Second International Workshop, NFMCP 2013, Held in Conjunction with ECML-PKDD 2013, Prague, Czech Republic, September 27, 2013, Revised Selected Papers /

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Material type: Text

TextSeries: Lecture Notes in Artificial Intelligence ; 8399Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Edition: 1st ed. 2014Description: XII, 261 p. 80 illus. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783319084077

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

006.312 23

LOC classification:

QA76.9.D343

Online resources:

Click here to access online

Contents:

Data Streams and and Time Series Analysis -- Classification, Clustering and Pattern Discovery -- Graphs, Networks and Relational Data -- Machine Learning and Music Data.

In: Springer Nature eBookSummary: This book constitutes the thoroughly refereed post-conference proceedings of the Second International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2013, held in conjunction with ECML/PKDD 2013 in Prague, Czech Republic, in September 2013. The 16 revised full papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on data streams and time series analysis, classification, clustering and pattern discovery, graphs, networks and relational data, machine learning and music data.

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Data Streams and and Time Series Analysis -- Classification, Clustering and Pattern Discovery -- Graphs, Networks and Relational Data -- Machine Learning and Music Data.

This book constitutes the thoroughly refereed post-conference proceedings of the Second International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2013, held in conjunction with ECML/PKDD 2013 in Prague, Czech Republic, in September 2013. The 16 revised full papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on data streams and time series analysis, classification, clustering and pattern discovery, graphs, networks and relational data, machine learning and music data.

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