Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories
Language: English
Published by Birkhäuser, 2018
Series: Book 278 of 322 - SpringerBriefs in Computer Science
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Add to basketItem description from seller
Druck auf Anfrage Neuware - Printed after ordering - This SpringerBrief provides an overview within data mining of spatiotemporal frequentpattern mining from evolving regions to the perspective of relationshipmodeling among the spatiotemporal objects, frequent pattern miningalgorithms, and data access methodologies for mining algorithms. Whilethe focus of this book is to provide readers insight into the miningalgorithms from evolving regions, the authors also discuss datamanagement for spatiotemporal trajectories, which has becomeincreasingly important with the increasing volume of trajectories.This brief describes state-of-the-art knowledge discoverytechniques to computer science graduatestudents who are interested in spatiotemporal data mining, as well asresearchers/professionals, who deal with advanced spatiotemporal dataanalysis in their fields. These fields include GIS-experts, meteorologists,epidemiologists, neurologists, and solar physicists.
Seller Inventory # 9783319998725
- Title
- Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories
- Author
- Berkay Aydin
- Publisher
- Birkhäuser
- Publication year
- 2018
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3319998722
- ISBN 13
- 9783319998725
- Item weight
- 195 grams
- Dimensions
- 235x155x7 mm
- Series
- Book 278 of 322: SpringerBriefs in Computer Science
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.
"Synopsis" may belong to another edition of this title.
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