Detecting localized homogeneous anomalies over spatio-temporal data

Aditya Telang, P. Deepak, Salil Joshi, Prasad Deshpande, Ranjana Rajendran

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)
708 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1480-1502
Number of pages23
JournalData Mining and Knowledge Discovery
Issue number5
Early online date15 Jul 2014
Publication statusPublished - Sep 2014

Bibliographical note

Special issue: ECML PKDD 2014


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