A Comparison of Hash-Based Methods for Trajectory Clustering

Maede Rayatidamavandi, Yu Zhuang, Mahshid Rahnamay-Naeini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The development of location-acquisition technologies has led to the emergence of massive spatial trajectory data. Recently many researchers have focused on techniques related to processing, managing and mining trajectories to extract knowledge and predictions useful for various applications. One of the first steps in processing trajectories is clustering and classification. Hash-based methods have been used and showed to be successful in clustering the large trajectory datasets. In this paper, we specifically focus on methods based on two types of hash functions: Locality-Sensitive and Distance-Based hash functions and compare them in terms of accuracy and bucket size balance. Our results suggest that, in comparison to Distance- Based hashes, Locality-Sensitive hashes results in higher accuracy but not necessarily higher bucket balance.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages107-112
Number of pages6
ISBN (Electronic)9781538619551
DOIs
StatePublished - Mar 29 2018
Event15th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017 - Orlando, United States
Duration: Nov 6 2017Nov 11 2017

Publication series

NameProceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017
Volume2018-January

Conference

Conference15th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017
CountryUnited States
CityOrlando
Period11/6/1711/11/17

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Keywords

  • Distance-Based Hashing
  • Locality Sensitive Hashing
  • Location Acquisition Technologies
  • Trajectory Classification
  • Trajectory Clustering

Cite this

Rayatidamavandi, M., Zhuang, Y., & Rahnamay-Naeini, M. (2018). A Comparison of Hash-Based Methods for Trajectory Clustering. In Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017 (pp. 107-112). (Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.32