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Time Series Clustering and Classification by Elizabeth Ann Maharaj Paperback Boo
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Time Series Clustering and Classification by Elizabeth Ann Maharaj Paperback Boo
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Time Series Clustering and Classification by Elizabeth Ann Maharaj Paperback Boo

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    eBay-Artikelnr.:365850494733
    Zuletzt aktualisiert am 09. Okt. 2025 14:21:52 MESZAlle Änderungen ansehenAlle Änderungen ansehen

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    ISBN-13
    9781032093499
    Book Title
    Time Series Clustering and Classification
    ISBN
    9781032093499
    Kategorie

    Über dieses Produkt

    Product Identifiers

    Publisher
    CRC Press LLC
    ISBN-10
    1032093498
    ISBN-13
    9781032093499
    eBay Product ID (ePID)
    14074976678

    Product Key Features

    Number of Pages
    246 Pages
    Publication Name
    Time Series Clustering and Classification
    Language
    English
    Subject
    Machine Theory, Probability & Statistics / General, General
    Publication Year
    2021
    Type
    Textbook
    Author
    Pierpaolo D'urso, Elizabeth Ann Maharaj, Jorge Caiado
    Subject Area
    Mathematics, Computers, Reference
    Series
    Chapman and Hall/Crc Computer Science and Data Analysis Ser.
    Format
    Trade Paperback

    Dimensions

    Item Weight
    16 Oz
    Item Length
    9.2 in
    Item Width
    6.1 in

    Additional Product Features

    Dewey Edition
    23
    Reviews
    "The book represents 20 years of research by the authors. They have achieved the goal of gathering in one place a broad spectrum of clustering and classification techniques for time series, which have attracted substantial attention for the last few decades...The book contains a number of examples of clustering, which are intended to highlight the main theoretical models on real data...The book contains a large amount of theoretical information and practical examples and may be recommended as a desk book for young scientists and applied mathematicians." - Maria Ivanchuk, ISCB News, July 2020 "The authors of this book have more than 20 years of experience on the topic of time series clustering and classification. They consolidate many important methods and algorithms commonly used in time series clustering and classification practices published by various scientific journals. In addition, they provide Matlab and R code and corresponding datasets to reproduce the examples in the book...This book covers most classical and common techniques for time series clustering and classification. It consolidates different methods into an extensive coherent framework. This makes the book a good reference for students and researchers." - Ming Chen, JASA, August 2020
    Illustrated
    Yes
    Dewey Decimal
    519.55
    Table Of Content
    1. Introduction 2. Time Series Features and Models 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 9. Feature-based classification approaches 10. Other time series classification approaches 11.Software and Data Sets
    Synopsis
    This book 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., 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
    LC Classification Number
    QA280.M3345 2021

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