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Permanent link (DOI): https://doi.org/10.7939/R34H1N

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Theses and Dissertations

A Data Cleaning Framework for Trajectory Clustering Open Access

Descriptions

Other title
Subject/Keyword
Clustering
GPS
Preprocessing
Trajectory
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Idrissov, Agzam Y.
Supervisor and department
Nascimento, Mario A. (Computing Science)
Examining committee member and department
Yang, Herb (Computing Science)
Nascimento, Mario A. (Computing Science)
Yuan, Li-Yan (Computing Science)
Barbosa, Denilson (Computing Science) - Chair
Department
Department of Computing Science
Specialization

Date accepted
2012-09-28T07:38:16Z
Graduation date
2012-09
Degree
Master of Science
Degree level
Master's
Abstract
Recent proliferation of low-cost and lightweight GPS tracking devices led to a large increase in the amounts of collected mobility data. The rapidly emerging field of location-based services requires accurate and informative knowledge mining from these large quantities of data. One such mobility knowledge mining task is trajectory clustering, where one tries to find paths that have been travelled frequently. Most existing trajectory clustering techniques do not discuss cleaning the data before applying a clustering algorithm. Since “noisy” data can have a significant effect on the clustering process, preprocessing such trajectory data will likely improve trajectory clustering results. In this thesis, we present a trajectory data cleaning framework, which consists of four steps: Outlier Detection, Stop Detection, Interpolation and Map Matching. We evaluate our framework using popular clustering algorithms and distance functions, and show that our proposed preprocessing (cleaning) framework indeed does improve the quality of obtained clusters.
Language
English
DOI
doi:10.7939/R34H1N
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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