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Time series clustering and classification/ Elizabeth Ann Maharaj, Pierpaolo D'Urso and Jorge Caiado

By: Contributor(s): Series: Computer Science and Data Analysis SeriesPublication details: Boca Raton: CRC, 2019Description: xv, 228 pages, 24 cmISBN:
  • 9781498773218
Subject(s): DDC classification:
  • 23 000SA.3 M214
Contents:
1. Introduction -- 2. Time series features and models -- I Unsupervised Approaches: Clustering techniques for Time Series -- 3. Traditional cluster analysis -- 4. Fuzzy clustering -- 5. Observation-based clustering -- 6. Feature-based clustering -- 7. Model-based clustering -- 8. Other time series clustering approaches -- II Supervised Approaches: Classification techniques for Time Series -- 9. feature- based approaches -- 10. Other time series classification approaches -- III Software and Data Sets -- 11. Software and data sets -- Bibliography -- Subject index
Summary: 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
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SA.3 M214 (Browse shelf(Opens below)) Available 138500
Total holds: 0

Includes bibliographical references and index

1. Introduction -- 2. Time series features and models -- I Unsupervised Approaches: Clustering techniques for Time Series -- 3. Traditional cluster analysis -- 4. Fuzzy clustering -- 5. Observation-based clustering -- 6. Feature-based clustering -- 7. Model-based clustering -- 8. Other time series clustering approaches -- II Supervised Approaches: Classification techniques for Time Series -- 9. feature- based approaches -- 10. Other time series classification approaches -- III Software and Data Sets -- 11. Software and data sets -- Bibliography -- Subject index

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

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