Fast mining of distance-based outliers in high-dimensional datasets

• Autores: Amol Ghoting, Srinivasan Parthasarathy, Matthew Eric Otey
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 16, Nº 3, 2008, págs. 349-364
• Idioma: inglés
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• Resumen

  • Defining outliers by their distance to neighboring data points has been shown to be an effective non-parametric approach to outlier detection. In recent years, many research efforts have looked at developing fast distance-based outlier detection algorithms. Several of the existing distance-based outlier detection algorithms report log-linear time performance as a function of the number of data points on many real low-dimensional datasets. However, these algorithms are unable to deliver the same level of performance on high-dimensional datasets, since their scaling behavior is exponential in the number of dimensions. In this paper, we present RBRP, a fast algorithm for mining distance-based outliers, particularly targeted at high-dimensional datasets. RBRP scales log-linearly as a function of the number of data points and linearly as a function of the number of dimensions. Our empirical evaluation demonstrates that we outperform the state-of-the-art algorithm, often by an order of magnitude.