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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.
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Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories
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Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories (Paperback)
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Paperback. Etat : new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783319998725
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Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories
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Springer, 2018
ISBN 10 : 3319998722
ISBN 13 : 9783319998725
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Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories
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Springer International Publishing Okt 2018, 2018
ISBN 10 : 3319998722
ISBN 13 : 9783319998725
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 120 pp. Englisch. N° de réf. du vendeur 9783319998725
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