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کامپیوتر و شبکه::
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CBRW estimates the outlierness of each feature value by captur- ing both intra- and inter-feature value couplings.
By inter-feature value couplings, we score feature values based on its interactions with values of other features.
For example, compared to the noisy value 'bachelor', although the outlying value 'low' has lower outlierness by only con- sidering intra-feature value couplings, it has much higher out- lierness when adding inter-feature value couplings, because it has stronger couplings with the exceptional value 'divorced'.
It estimates the outlierness of each feature value by modelling intra- and inter-feature value couplings via biased random walks on an attribute graph to tackle the two aforementioned issues.
The topological structure of the graph is built on inter-feature value couplings, while the node property is obtained based on intra-feature value cou- plings.
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