Article

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

Data Mining and Knowledge Discovery (Impact Factor: 1.74). 05/2008; 16(3):349-364. DOI: 10.1007/s10618-008-0093-2
Source: DBLP

ABSTRACT 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.

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    • "To make ORCA fast, Ghoting et. al [8] proposed RBRP (Recursive Binning and Re-Projection) method. The method is a two-step algorithm that improves the pruning rule by setting up to find nearer point easily and hasty. "
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    • "However recent research has focused on categorical attributes (e.g. [3], [4] and [5]). "
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    • "Local outlier ranking based on density deviation in local neighborhoods has first been proposed by LOF [7]. In recent years, this outlier mining paradigm has been extended by enhanced scoring functions and efficient outlier ranking algorithms [25], [5], [13], [19], [17], [23], [9]. "
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