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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorVelegrakis, Ioannis
dc.contributor.authorIliakis, Manolis
dc.date.accessioned2022-09-13T00:00:38Z
dc.date.available2022-09-13T00:00:38Z
dc.date.issued2022
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/42763
dc.description.abstractAn outlier is a point that deviates significantly from the pattern that has been formed from the majority of the data points. The presence of outliers can exacerbate statistical results which leads to misrepresented relationships between different data and faulty conclusions based on them. This is an urgent issue in a data driven world and people in the data sector are following harsh and tedious procedures to deal with that. In the present article, an half automated tool for outlier detection returning a single score for a structured dataset is proposed with minimal human intervention. This tool can either be used in a python environment or directly in a python script.
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectAnomaly Detection Techniques on relational data as Quality Evaluation of a dataset
dc.titleAnomaly Detection Techniques on relational data as Quality Evaluation of a dataset
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsOutlier detection tool; Autoencoder; ngrams; Isolation Forest; Lightweight Online detection of anomalies
dc.subject.courseuuApplied Data Science
dc.thesis.id10545


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