Group anomaly detection for spatio-temporal collective behaviour scenarios in smart cities-Reference-Cited by-同舟云学术

Group anomaly detection for spatio-temporal collective behaviour scenarios in smart cities

Published:2022-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 15th ACM SIGSPATIAL International Workshop on Computational Transportation Science
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Author:

Lohrer Andreas¹,Binder Johannes Josef²,Kröger Peer³

Affiliation:

1. Ludwig-Maximilians-Universität München, München, Bavaria, Germany and Christian-Albrechts-Universität zu Kiel, Kiel, Schleswig-Holstein, Germany

2. Ludwig-Maximilians-Universität München, München, Bavaria, Germany

3. Christian-Albrechts-Universität zu Kiel, Kiel, Schleswig-Holstein, Germany

Funder

Bundesministerium für Bildung und Forschung

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3557991.3567801

Reference10 articles.

1. Raghavendra Chalapathy , Edward Toth , and Sanjay Chawla . 2018 . Group anomaly detection using deep generative models . In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 173--189 . Raghavendra Chalapathy, Edward Toth, and Sanjay Chawla. 2018. Group anomaly detection using deep generative models. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 173--189.

2. O.Y. Ercoskun. 2011. Green and Ecological Technologies for Urban Planning: Creating Smart Cities: Creating Smart Cities. Information Science Reference. O.Y. Ercoskun. 2011. Green and Ecological Technologies for Urban Planning: Creating Smart Cities: Creating Smart Cities. Information Science Reference.

3. On the nature and types of anomalies: a review of deviations in data

4. Taste variation in environmental features of bicycle routes

5. Finding Rats in Cats: Detecting Stealthy Attacks using Group Anomaly Detection

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