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FINDING ANOMALOUS TIME FRAMES IN REAL-WORLD LOG DATA
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Anomaly detection is a huge fi€eld of research focused on the task of €finding weird or outlying points in data. Th‘is task is useful in all fi€elds that handle large amounts of data and is therefore a big topic of research. Th‘e focus of research often lies in fi€nding novel approaches for €finding anomalies in already labeled and well-understood data. ‘This thesis will not focus on a novel algorithm but instead display and discuss the power of an anomaly detection process that focuses on feature engineering and feature exploration. Th‘e thesis will also compare two unsupervised anomaly classifi€cation algorithms, namely k-nearest neighbours and principal component analysis, in terms of explainability and scalability. ‘The results concludes that sometimes feature engineering can display anomalies just as well as novel and complex anomaly detection algorithms.

Place, publisher, year, edition, pages
2019. , p. 30
Series
UMNAD ; 1188
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-163311OAI: oai:DiVA.org:umu-163311DiVA, id: diva2:1351115
External cooperation
ICT
Educational program
Bachelor of Science Programme in Computing Science
Supervisors
Examiners
Available from: 2019-09-13 Created: 2019-09-13 Last updated: 2019-09-13Bibliographically approved

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CiteExportLink to record
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