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کامپیوتر و شبکه::
قدم زدن تصادفی سوگیری شده و جفت شده
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.
CBRW estimates outlier scores of feature values
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.
In this paper, we introduce a new unsupervised outlier detection method, namely Coupled Biased Random Walk (CBRW), to identify outliers in those complex data.
CBRW estimates the outlierness of each feature value by captur- ing both intra- and inter-feature value couplings.
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