Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories / by Berkay Aydin, Rafal. A Angryk

Von:

Aydin, Berkay [VerfasserIn]

Mitwirkende(r):

Angryk, Rafal. A [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: SpringerBriefs in Computer Science | SpringerLink BücherVerlag: Cham : Springer International Publishing, 2018Beschreibung: Online-Ressource (XIII, 106 p. 33 illus., 32 illus. in color, online resource)ISBN:

9783319998732

Schlagwörter:

Andere physische Formen: 9783319998725 | Erscheint auch als: 978-3-319-99872-5 Druck-AusgabeDDC-Klassifikation:

005.7

LOC-Klassifikation:

QA75.5-76.95

DOI: DOI: 10.1007/978-3-319-99873-2Online-Ressourcen:

Zusammenfassung: 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 physicistsPPN: PPN: 1036398587Package identifier: Produktsigel: ZDB-2-SCS | ZDB-2-SEB | ZDB-2-SXCS

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