dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Velegrakis, Ioannis | |
dc.contributor.author | Iliakis, Manolis | |
dc.date.accessioned | 2022-09-13T00:00:38Z | |
dc.date.available | 2022-09-13T00:00:38Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/42763 | |
dc.description.abstract | An 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.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | Anomaly Detection Techniques on relational data as Quality Evaluation of a dataset | |
dc.title | Anomaly Detection Techniques on relational data as Quality Evaluation of a dataset | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | Outlier detection tool; Autoencoder; ngrams; Isolation Forest; Lightweight Online detection of anomalies | |
dc.subject.courseuu | Applied Data Science | |
dc.thesis.id | 10545 | |