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Outlier Detection in Complex Categorical Data by Modelling the Feature Value Couplings
Detecting outliers in categorical data has wide applications in various domains, such as intrusion detection, fraud detection, and early detection of diseases, where categorical features are the only or indispensable fea- tures for describing data objects [Chandola et al.
Many real-world categorical data sets own one or both of the following key characteristics: (i) there are diversified fre- quency distributions across different features (e.g.
By contrast, signifi- cantly less research has been conducted on categorical data.
Existing methods on categorical data are mainly pattern- based methods, which search for infrequent/frequent patterns as outlying/normal patterns through different methods (e.g.
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