MOMENTAN AUSVERKAUFT
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Product Identifiers
PublisherCRC Press LLC
ISBN-101032093498
ISBN-139781032093499
eBay Product ID (ePID)14074976678
Product Key Features
Number of Pages246 Pages
Publication NameTime Series Clustering and Classification
LanguageEnglish
SubjectMachine Theory, Probability & Statistics / General, General
Publication Year2021
TypeTextbook
AuthorPierpaolo D'urso, Elizabeth Ann Maharaj, Jorge Caiado
Subject AreaMathematics, Computers, Reference
SeriesChapman and Hall/Crc Computer Science and Data Analysis Ser.
FormatTrade Paperback
Dimensions
Item Weight16 Oz
Item Length9.2 in
Item Width6.1 in
Additional Product Features
Dewey Edition23
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
IllustratedYes
Dewey Decimal519.55
Table Of Content1. 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
SynopsisThis 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 NumberQA280.M3345 2021
