Data mining application to healthcare fraud detection: a two-step unsupervised clustering method for outlier detection with administrative databases

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1472-6947

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Data mining application to healthcare fraud detection: a two-step unsupervised clustering method for outlier detection with administrative databases ; volume:20 ; number:1 ; day:14 ; month:7 ; year:2020 ; pages:1-11 ; date:12.2020
BMC medical informatics and decision making ; 20, Heft 1 (14.7.2020), 1-11, 12.2020

Classification: Soziale Probleme, Sozialdienste, Versicherungen

Creator: Massi, Michela Carlotta
Ieva, Francesca
Lettieri, Emanuele

Contributor: SpringerLink (Online service)

DOI: 10.1186/s12911-020-01143-9

URN: urn:nbn:de:101:1-2020091020513983781035

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:47 AM CEST

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Massi, Michela Carlotta
Ieva, Francesca
Lettieri, Emanuele
SpringerLink (Online service)

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Data mining application to healthcare fraud detection: a two-step unsupervised clustering method for outlier detection with administrative databases

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Other Objects (12)

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FairLOF: Fairness in Outlier Detection

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Learning low-dimensional manifolds under the L0-norm constraint for unsupervised outlier detection

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Subgroup and outlier detection analysis

Randomized outlier detection with trees

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