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Cluster-Centric Anomaly Detection and Characterization in Spatial Time Series Open Access


Other title
Structure visualization
Spatial time series
Anomaly detection
Type of item
Degree grantor
University of Alberta
Author or creator
Izakian, Hesam
Supervisor and department
Witold Pedrycz (Department of Electrical and Computer Engineering)
Examining committee member and department
Marek Reformat (Department of Electrical and Computer Engineering)
Aminah Robinson Fayek (Department of Civil and Environmental Engineering)
Vladik Kreinovich (Computer Science, University of Texas at El Paso)
Petr Musilek (Department of Electrical and Computer Engineering)
Witold Pedrycz (Department of Electrical and Computer Engineering)
Department of Electrical and Computer Engineering
Software Engineering and Intelligent Systems
Date accepted
Graduation date
Doctor of Philosophy
Degree level
Anomaly detection in spatial time series is a challenging problem with numerous potential applications. A comprehensive anomaly detection approach not only should be able to detect and identify the emerging anomalies, but it also has to characterize the essence of these anomalies by visualizing the structures revealed within data in a way, which is understandable to the end-user. In this study, a cluster-centric framework for anomaly detection and characterization in spatial time series has been developed. For this purpose, the time series part of data is divided into a set of subsequences and the available spatio-temporal structures within the generated subsequences are discovered through a fuzzy clustering technique. Since in spatial time series, each datum is composed of features dealing with the spatial and the temporal (one or more time series) components, clustering of data of this nature poses some significant challenges, especially in terms of a suitable treatment of different components of the data. We propose an extended version of the Fuzzy C-Means (FCM) clustering by introducing a composite distance function with adjustable weights (parameters) controlling the impact of different components in the clustering process. Three optimization criteria - a reconstruction error, a prediction error, and an agreement level are introduced and used as a vehicle to quantify the performance of the clustering method. By comparing the revealed structures (clusters) in spatial time series in successive time intervals, one assigns an anomaly score to each cluster measuring the level of unexpected changes in data. Moreover, through developing some fuzzy relational dependencies, the propagation of anomalies can be visualized in an understandable way to the end-user. To illustrate the proposed technique in this study, several datasets including synthetic and real-world data have been investigated. Experimental studies show that the proposed technique is able to find incident anomalies and quantify the propagation of anomalies over time.
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.
Citation for previous publication
H. Izakian, and W. Pedrycz, “Anomaly Detection in Time Series Data using a Fuzzy C-Means Clustering,” Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), Edmonton, Canada, pp. 1513-1518, 2013, IEEE Press.H. Izakian, W. Pedrycz, and I. Jamal, “Clustering spatio-temporal data: An augmented fuzzy C-Means,” IEEE Transactions on Fuzzy Systems, vol. 21, no. 5, pp. 855 - 868, 2013.H. Izakian and W. Pedrycz, “Agreement-Based Fuzzy C-Means for Clustering Data with Blocks of Features,” Neurocomputing, vol. 127, pp. 266-280, 2014.H. Izakian and W. Pedrycz, “Anomaly Detection and Characterization in Spatial Time Series Data: A Cluster-Centric Approach,” IEEE Transactions on Fuzzy Systems, DOI: 10.1109/TFUZZ.2014.2302456, 2014.

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