Abstract
The last decade has witnessed an unprecedented growth in availability of data having spatio-temporal characteristics. Given the scale and richness of such data, finding spatio-temporal patterns that demonstrate significantly different behavior from their neighbors could be of interest for various application scenarios such as—weather modeling, analyzing spread of disease outbreaks, monitoring traffic congestions, and so on. In this paper, we propose an automated approach of exploring and discovering such anomalous patterns irrespective of the underlying domain from which the data is recovered. Our approach differs significantly from traditional methods of spatial outlier detection, and employs two phases—(i) discovering homogeneous regions, and (ii) evaluating these regions as anomalies based on their statistical difference from a generalized neighborhood. We evaluate the quality of our approach and distinguish it from existing techniques via an extensive experimental evaluation.
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In this paper, we extensively use color-based figures to illustrate the concepts of anomalies. Hence, we request the reader to refer to the electronic version or a colored printout of the paper for better readability.
For sake of clarity, we illustrate a spatial grid; however, the formulation is extendible to the temporal dimension.
We do not include outlier detection techniques in our comparative analysis since it is not clear as to how outlier detection techniques that estimate divergent behavior at each data object level may be fairly compared with techniques that discover groups of objects that exhibit divergent behavior.
It must be noted that conducting user surveys is a difficult task. Hence, we conducted the user survey on \(Dataset_1\) only and not on on \(Dataset_2\).
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Telang, A., Deepak, P., Joshi, S. et al. Detecting localized homogeneous anomalies over spatio-temporal data. Data Min Knowl Disc 28, 1480–1502 (2014). https://doi.org/10.1007/s10618-014-0366-x
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DOI: https://doi.org/10.1007/s10618-014-0366-x