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Time series clustering and classification

The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and...

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Detalles Bibliográficos
Autores principales: Maharaj, Elizabeth Ann, D'Urso, Pierpaolo, Caiado, Jorge
Lenguaje:eng
Publicado: Productivity Press 2019
Materias:
Acceso en línea:http://cds.cern.ch/record/2669262
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author Maharaj, Elizabeth Ann
D'Urso, Pierpaolo
Caiado, Jorge
author_facet Maharaj, Elizabeth Ann
D'Urso, Pierpaolo
Caiado, Jorge
author_sort Maharaj, Elizabeth Ann
collection CERN
description The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes relevant developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students. Features Provides an overview of the methods and applications of pattern recognition of time series Covers a wide range of techniques, including unsupervised and supervised approaches Includes a range of real examples from medicine, finance, environmental science, and more R and MATLAB code, and relevant data sets are available on a supplementary website
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institution Organización Europea para la Investigación Nuclear
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spelling cern-26692622021-04-21T18:26:51Zhttp://cds.cern.ch/record/2669262engMaharaj, Elizabeth AnnD'Urso, PierpaoloCaiado, JorgeTime series clustering and classificationComputing and ComputersThe beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes relevant developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students. Features Provides an overview of the methods and applications of pattern recognition of time series Covers a wide range of techniques, including unsupervised and supervised approaches Includes a range of real examples from medicine, finance, environmental science, and more R and MATLAB code, and relevant data sets are available on a supplementary websiteProductivity Pressoai:cds.cern.ch:26692622019
spellingShingle Computing and Computers
Maharaj, Elizabeth Ann
D'Urso, Pierpaolo
Caiado, Jorge
Time series clustering and classification
title Time series clustering and classification
title_full Time series clustering and classification
title_fullStr Time series clustering and classification
title_full_unstemmed Time series clustering and classification
title_short Time series clustering and classification
title_sort time series clustering and classification
topic Computing and Computers
url http://cds.cern.ch/record/2669262
work_keys_str_mv AT maharajelizabethann timeseriesclusteringandclassification
AT dursopierpaolo timeseriesclusteringandclassification
AT caiadojorge timeseriesclusteringandclassification