Spatiotemporal frequent pattern mining from evolving region trajectories /

Spatiotemporal frequent pattern mining from evolving region trajectories /

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Bibliographic Details
Author / Creator:	Aydin, Berkay, author.
Imprint:	Cham, Switzerland : Springer, 2018.
Description:	1 online resource : color illustrations
Language:	English
Series:	SpringerBriefs in Computer Science SpringerBriefs in computer science.
Subject:	Spatial analysis (Statistics) Data mining. Geospatial data. Geographic information systems. Data mining. Geographic information systems. Geospatial data. Spatial analysis (Statistics)
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11718326

Hidden Bibliographic Details
Other authors / contributors:	Angryk, Rafal A., author.
ISBN:	9783319998732 3319998730 9783319998749 3319998749 9783319998725 3319998722
Digital file characteristics:	text file PDF
Notes:	Includes bibliographical references and index. Online resource; title from PDF title page (EBSCO, viewed October 18, 2018).
Summary:	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.
Other form:	Print version: Aydin, Berkay. Spatiotemporal frequent pattern mining from evolving region trajectories. Cham, Switzerland : Springer, 2018 3319998722 9783319998725
Standard no.:	10.1007/978-3-319-99873-2 10.1007/978-3-319-99

Table of Contents:

A gentle introduction to spatiotemporal data mining
Modeling spatiotemporal trajectories
Modeling spatiotemporal relationships among trajectories
Significance measurements for spatiotemporal co-occurences
Spatiotemporal co-occurence pattern (STCOP) mining
Spatiotemporal event sequence (STES) mining.