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کامپیوتر و شبکه::
ویژگی نویزی
This paper introduces a novel unsupervised outlier detection method, namely Coupled Biased Random Walks (CBRW), for identifying outliers in categori- cal data with diversified frequency distributions and many noisy features.
Substantial experiments show that CBRW can not only detect outliers in complex data significantly better than the state-of-the-art methods, but also greatly improve the performance of existing meth- ods on data sets with many noisy features.
, one is divorced and has low income) to successfully spot the cheater, while it is difficult to derive effective outlying/normal patterns with the presence of the noisy features 'Gender' and 'Education'.
This enables CBRW to distinct outlying values from noisy feature values, as noisy values are supposed to have weak couplings with outlying values.
Substantial experiments show that (1) our CBRW-based outlier detection method significantly outperforms the state- of-the-art methods on complex data sets; (2) without the costly pattern searching, our method runs much faster than the pattern-based methods; and (3) the CBRW-based feature selection method greatly lifts the pattern-based outlier detec- tors on data sets with many noisy features in terms of both accuracy and efficiency.
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