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Time series clustering and classification

By: Maharaj, Elizabeth Ann.
Contributor(s): D'Urso, Pierpaolo [Co author] | Caiado, Jorge [Co author].
Material type: materialTypeLabelBookSeries: Chapman & Hall/CRC Computer Science & Data Analysis Series. Publisher: Boca Raton CRC press 2019Description: xv, 228 p. Includes bibliographical references and index.ISBN: 9781498773218.Subject(s): Time-series analysis | Cluster analysis | Probability - Statistics - General | Machine theoryDDC classification: 519.55 Summary: 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 https://www.crcpress.com/Time-Series-Clustering-and-Classification/Maharaj-DUrso-Caiado/p/book/9781498773218
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Slot 1678 (2 Floor, East Wing) Non-fiction 519.55 M2T4 (Browse shelf) Not for Issue 200258

Table of contents:

Introduction
Time Series Features and Models
Traditional cluster analysis
Fuzzy clustering
Observation-based clustering
Feature-based clustering
Model-based clustering
Other time series clustering approaches
Feature-based classification approaches
Other time series classification approaches
Software and Data Sets




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

https://www.crcpress.com/Time-Series-Clustering-and-Classification/Maharaj-DUrso-Caiado/p/book/9781498773218

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