Heinz Koeppl, Marc Hafner, et al.
BMC Bioinformatics
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. © 2014 The Author(s).
Heinz Koeppl, Marc Hafner, et al.
BMC Bioinformatics
Robert E. Donovan
INTERSPEECH - Eurospeech 2001
Frank R. Libsch, Takatoshi Tsujimura
Active Matrix Liquid Crystal Displays Technology and Applications 1997
B.K. Boguraev, Mary S. Neff
HICSS 2000