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Spatiotemporal frequent pattern mini...
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Angryk, Rafal. A.
Spatiotemporal frequent pattern mining from evolving region trajectories
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Spatiotemporal frequent pattern mining from evolving region trajectories/ by Berkay Aydin, Rafal. A Angryk.
Author:
Aydin, Berkay.
other author:
Angryk, Rafal. A.
Published:
Cham :Springer International Publishing : : 2018.,
Description:
xiii, 106 p. :ill., digital ; : 24 cm.;
Contained By:
Springer eBooks
Subject:
Spatial analysis (Statistics) -
Online resource:
https://doi.org/10.1007/978-3-319-99873-2
ISBN:
9783319998732
Spatiotemporal frequent pattern mining from evolving region trajectories
Aydin, Berkay.
Spatiotemporal frequent pattern mining from evolving region trajectories
[electronic resource] /by Berkay Aydin, Rafal. A Angryk. - Cham :Springer International Publishing :2018. - xiii, 106 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - 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.
ISBN: 9783319998732
Standard No.: 10.1007/978-3-319-99873-2doiSubjects--Topical Terms:
562936
Spatial analysis (Statistics)
LC Class. No.: QA76.9.D343
Dewey Class. No.: 006.312
Spatiotemporal frequent pattern mining from evolving region trajectories
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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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