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Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories (SpringerBriefs in Computer Science) - Softcover
Synopsis
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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Springer International Publishing AG, Cham, 2018
ISBN 10: 3319998722
ISBN 13: 9783319998725
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Paperback. Condition: 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. Seller Inventory # 9783319998725
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Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories (SpringerBriefs in Computer Science)
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