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Mining At Most Top-K% Spatiotemporal Co-occurrence Patterns in Datasets with Extended Spatial Representations

Authors:

Karthik Ganesan Pillai,

Rafal A. Angryk,

Dustin Kempton,

Petrus C. MartensAuthors Info & Claims

ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 2, Issue 3

Article No.: 10, Pages 1 - 27

https://doi.org/10.1145/2936775

Published: 26 September 2016 Publication History

Abstract

Spatiotemporal co-occurrence patterns (STCOPs) in datasets with extended spatial representations are two or more different event types, represented as polygons evolving in time, whose instances often occur together in both space and time. Finding STCOPs is an important problem in domains such as weather monitoring, wildlife migration, and solar physics. Nevertheless, in real life, it is difficult to find a suitable prevalence threshold without prior domain-specific knowledge. In this article, we focus our work on the problem of mining at most top-K% of STCOPs from continuously evolving spatiotemporal events that have polygon-like representations, without using a user-specified prevalence threshold.

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Cited By

Sharmiladevi SSiva Sathya S(2021)Evaluation Metrics of Spatial and Spatiotemporal Data Mining TechniquesEmerging Technologies in Data Mining and Information Security10.1007/978-981-15-9774-9_42(449-463)Online publication date: 5-May-2021
https://doi.org/10.1007/978-981-15-9774-9_42
Aydin BAngryk RAydin BAngryk R(2018)Spatiotemporal Event Sequence (STES) MiningSpatiotemporal Frequent Pattern Mining from Evolving Region Trajectories10.1007/978-3-319-99873-2_6(71-96)Online publication date: 16-Oct-2018
https://doi.org/10.1007/978-3-319-99873-2_6
Aydin BAngryk RAydin BAngryk R(2018)Spatiotemporal Co-occurrence Pattern (STCOP) MiningSpatiotemporal Frequent Pattern Mining from Evolving Region Trajectories10.1007/978-3-319-99873-2_5(55-69)Online publication date: 16-Oct-2018
https://doi.org/10.1007/978-3-319-99873-2_5

Index Terms

Mining At Most Top-K% Spatiotemporal Co-occurrence Patterns in Datasets with Extended Spatial Representations
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

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Published In

cover image ACM Transactions on Spatial Algorithms and Systems

ACM Transactions on Spatial Algorithms and Systems Volume 2, Issue 3

October 2016

129 pages

ISSN:2374-0353

EISSN:2374-0361

DOI:10.1145/3001646

Editor:
Hanan Samet
University of Maryland, College Park

Issue’s Table of Contents

Copyright © 2016 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 September 2016

Accepted: 01 May 2016

Revised: 01 December 2015

Received: 01 November 2013

Published in TSAS Volume 2, Issue 3

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Cited By

Sharmiladevi SSiva Sathya S(2021)Evaluation Metrics of Spatial and Spatiotemporal Data Mining TechniquesEmerging Technologies in Data Mining and Information Security10.1007/978-981-15-9774-9_42(449-463)Online publication date: 5-May-2021
https://doi.org/10.1007/978-981-15-9774-9_42
Aydin BAngryk RAydin BAngryk R(2018)Spatiotemporal Event Sequence (STES) MiningSpatiotemporal Frequent Pattern Mining from Evolving Region Trajectories10.1007/978-3-319-99873-2_6(71-96)Online publication date: 16-Oct-2018
https://doi.org/10.1007/978-3-319-99873-2_6
Aydin BAngryk RAydin BAngryk R(2018)Spatiotemporal Co-occurrence Pattern (STCOP) MiningSpatiotemporal Frequent Pattern Mining from Evolving Region Trajectories10.1007/978-3-319-99873-2_5(55-69)Online publication date: 16-Oct-2018
https://doi.org/10.1007/978-3-319-99873-2_5

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