Traditional outlier detection techniques usually fail to work efficiently on high-dimensional data due to the curse of dimensionality. This work proposes a novel method for subspace outlier detection, that specifically deals with multidimensional spaces where feature relevance is a local rather than a global property. Different from existing approaches, it is not grid-based and dimensionality unbiased. Thus, its performance is impervious to grid resolution as well as the curse of dimensionality. In addition, our approach ranks the outliers, allowing users to select the number of desired outliers, thus mitigating the issue of high false alarm rate. Extensive empirical studies on real datasets show that our approach efficiently and effectively detects outliers, even in high-dimensional spaces.
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