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Time Series Clustering and Classification

By: Contributor(s): Publication details: CRC Press 2019 Boca Raton, FloridaDescription: xv, 228 pagesISBN:
  • 9781498773218
Subject(s): DDC classification:
  • 519.55 MAH
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,
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Item type Current library Collection Call number Status Date due Barcode
Books Books Symbiosis Institute of Business Management - Hyderabad General Text Book 519.55 MAH (Browse shelf(Opens below)) Available SIBMH-B-10725
Browsing Symbiosis Institute of Business Management - Hyderabad shelves, Shelving location: General, Collection: Text Book Close shelf browser (Hides shelf browser)
519.535 TAB Using multivariate statistics 519.536 ARK Regression Analysis 519.536 FUL Ordered Regression Models 519.55 MAH Time Series Clustering and Classification 600 VER Material Management 600 VER Material Management 600 VER Material Management


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,

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