Detecting anomaly collections using extreme feature ranks

DOI10.1007/s10618-014-0360-3zbMath1405.62050OpenAlexW2024347029MaRDI QIDQ1715864

Hanbo Dai, HweeHwa Pang, Ee-Peng Lim, Feida Zhu

Publication date: 29 January 2019

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://ink.library.smu.edu.sg/sis_research/2534

zbMATH Keywords

spam detectionanomaly clusteranomaly collectionextreme feature rankoutlier groupspam cluster

Mathematics Subject Classification ID

Statistics of extreme values; tail inference (62G32)

Related Items (1)

Detecting cooperative and organized spammer groups in micro-blogging community

Uses Software

• LOF
• ROCK

Cites Work

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• Reverse search for enumeration
• Cluster-based outlier detection
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• A Multiple Comparison Procedure for Comparing Several Treatments with a Control
• Generating All Maximal Independent Sets: NP-Hardness and Polynomial-Time Algorithms
• An accurate computation of the hypergeometric distribution function
• Discovering cluster-based local outliers
• Properties of the Extended Hypergeometric Distribution

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