© 2018

Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories


Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Berkay Aydin, Rafal A. Angryk
    Pages 1-7
  3. Berkay Aydin, Rafal A. Angryk
    Pages 9-15
  4. Berkay Aydin, Rafal A. Angryk
    Pages 17-27
  5. Berkay Aydin, Rafal A. Angryk
    Pages 29-53
  6. Berkay Aydin, Rafal A. Angryk
    Pages 55-69
  7. Berkay Aydin, Rafal A. Angryk
    Pages 71-96
  8. Back Matter
    Pages 97-106

About this book


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.


frequent pattern mining data mining spatiotemporal trajectory evolving region spatiotemporal data mining spatiotemporal relationship spatiotemporal co-occurrence pattern spatiotemporal event sequence spatiotemporal access methods moving object trajectory modeling co-occurence event sequence

Authors and affiliations

  1. 1.Department of Computer ScienceGeorgia State UniversityAtlantaUSA
  2. 2.Department of Computer ScienceGeorgia State UniversityAtlantaUSA

Bibliographic information

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