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Data-driven dominance tree-based anomaly detection for mixed attribute data
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DOI：10.1016/j.neucom.2026.134708.png)
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Abstract
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• This paper proposes an anomaly detection method for identifying anomalies in complex data distributions. • Dominance tree is used to obtain higher-quality graph structures. • Cumulative connection strength is used to construct a path-based anomaly score. • The method is applicable to categorical, numeric, and mixed-type data.
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