Abstract
Many anomaly detection techniques consider all the data-space dimensions when looking for outliers, and some others consider only specific subspaces, in isolation from other subspaces. However, interesting information about anomalous data points is embedded in the inter-relationships of the subspaces within which the data points appear to be outliers. Important characteristics of a dataset can be revealed by looking at these inter-relationships among subspaces. We describe a methodology for searching for outliers within the context of contiguous subspaces in the subspace lattice of a domain. We demonstrate additional insights about the outliers gained from this approach compared to finding the outliers in only specific subspaces or in the complete data-space. This additional information points an analyst to peculiar sets of subspaces to investigate further the underlying structure of the data space and also of the anomalous nature of the data points.
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Joshi, V., Bhatnagar, R. (2014). Outlier Analysis Using Lattice of Contiguous Subspaces. In: Ślȩzak, D., Schaefer, G., Vuong, S.T., Kim, YS. (eds) Active Media Technology. AMT 2014. Lecture Notes in Computer Science, vol 8610. Springer, Cham. https://doi.org/10.1007/978-3-319-09912-5_20
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DOI: https://doi.org/10.1007/978-3-319-09912-5_20
Publisher Name: Springer, Cham
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