Abstract
Many real data sets are very high dimensional. In some scenarios, real data sets may contain hundreds or thousands of dimensions. With increasing dimensionality, many of the conventional outlier detection methods do not work very effectively. This is an artifact of the well known curse of dimensionality. In high-dimensional space, the data becomes sparse, and the true outliers become masked by the noise effects of multiple dimensions, when analyzed in full dimensionality.
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© 2013 Springer Science+Business Media New York
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Aggarwal, C.C. (2013). High-Dimensional Outlier Detection: The Subspace Method. In: Outlier Analysis. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6396-2_5
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DOI: https://doi.org/10.1007/978-1-4614-6396-2_5
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