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.
- Book Title Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories
- Series Title SpringerBriefs in Computer Science
- Series Abbreviated Title SpringerBriefs Computer Sci.
- DOI https://doi.org/10.1007/978-3-319-99873-2
- Copyright Information The Author(s), under exclusive license to Springer Nature Switzerland AG 2018
- Publisher Name Springer, Cham
- eBook Packages Computer Science Computer Science (R0)
- Softcover ISBN 978-3-319-99872-5
- eBook ISBN 978-3-319-99873-2
- 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
Information Systems and Communication Service
Geographical Information Systems/Cartography
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