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- W4320157560 abstract "Anomaly detection is an important task in many fields such as eHealth and online fraud. In this paper, we propose a new technique for anomaly detection based on a graph that connects transactions with the same attribute values and searches for dense clusters indicative of an anomalous pattern. The experimental evaluation shows that the graph-based approach outperforms two other approaches in the considered dataset. The extension of this approach to the eHealth domain is reserved as future work." @default.
- W4320157560 created "2023-02-13" @default.
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- W4320157560 date "2022-01-01" @default.
- W4320157560 modified "2023-09-27" @default.
- W4320157560 title "A Graph-Based Approach to Detect Anomalies Based on Shared Attribute Values" @default.
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- W4320157560 doi "https://doi.org/10.1007/978-3-031-24801-6_36" @default.
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