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Table of contents (6 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This SpringerBrief provides an overview within data mining of spatiotemporal frequent pattern mining from evolving regions to the perspective of relationship modeling among the spatiotemporal objects, frequent pattern mining algorithms, and data access methodologies for mining algorithms. While the focus of this book is to provide readers insight into the mining algorithms from evolving regions, the authors also discuss data management for spatiotemporal trajectories, which has become increasingly important with the increasing volume of trajectories.
This brief describes state-of-the-art knowledge discovery techniques to computer science graduate students who are interested in spatiotemporal data mining, as well as researchers/professionals, who deal with advanced spatiotemporal data analysis in their fields. These fields include GIS-experts, meteorologists, epidemiologists, neurologists, and solar physicists.
Authors and Affiliations
Bibliographic Information
Book Title: Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories
Authors: Berkay Aydin, Rafal. A Angryk
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-319-99873-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2018
Softcover ISBN: 978-3-319-99872-5Published: 18 October 2018
eBook ISBN: 978-3-319-99873-2Published: 15 October 2018
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XIII, 106
Number of Illustrations: 1 b/w illustrations, 32 illustrations in colour
Topics: Information Systems and Communication Service, Geographical Information Systems/Cartography, Regional/Spatial Science