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Clustering Based Outlier Detection for Improved Situation Awareness within Air Traffic Control
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Förbättrad översiktsbild inom flygtrafikledning med hjälp av klusterbaserad anomalidetektering (Swedish)
Abstract [en]

The aim of this thesis is to examine clustering based outlier detection algorithms on their ability to detect abnormal events in flight traffic. A nominal model is trained on a data-set containing only flights which are labeled as normal. A detection scoring function based on the nominal model is used to decide if a new and in forehand unseen data-point behaves like the nominal model or not. Due to the unknown structure of the data-set three different clustering algorithms are examined for training the nominal model, K-means, Gaussian Mixture Model and Spectral Clustering. Depending on the nominal model different methods to obtain a detection scoring is used, such as metric distance, probability and OneClass Support Vector Machine.

This thesis concludes that a clustering based outlier detection algorithm is feasible for detecting abnormal events in flight traffic. The best performance was obtained by using Spectral Clustering combined with a Oneclass Support Vector Machine. The accuracy on the test data-set was 95.8%. The algorithm managed to correctly classify 89.4% of the datapoints labeled as abnormal and correctly classified 96.2% of the datapoints labeled as normal.

Abstract [sv]

Syftet med detta arbete är att undersöka huruvida klusterbaserad anomalidetektering kan upptäcka onormala händelser inom flygtrafik. En normalmodell är anpassad till data som endast innehåller flygturer som är märkta som normala. Givet denna normalmodell så anpassas en anomalidetekteringsfunktion så att data-punkter som är lika normalmodellen klassificeras som normala och data-punkter som är avvikande som anomalier. På grund av att strukturen av nomraldatan är okänd så är tre olika klustermetoder testade, K-means, Gaussian Mixture Model och Spektralklustering. Beroende på hur normalmodellen är modellerad så har olika metoder för anpassa en detekteringsfunktion används, så som baserat på avstånd, sannolikhet och slutligen genom One-class Support Vector Machine.

Detta arbete kan dra slutsatsen att det är möjligt att detektera anomalier med hjälp av en klusterbaserad anomalidetektering. Den algoritm som presterade bäst var den som kombinerade spektralklustring med One-class Support Vector Machine. På test-datan så klassificerade algoritmen $95.8\%$ av all data korrekt. Av alla data-punkter som var märka som anomalier så klassificerade denna algoritm 89.4% rätt, och på de data-punkter som var märka som normala så klassificerade algoritmen 96.2% rätt.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:396
Keywords [en]
Applied Mathematics, Clustering, Spectral Clustering, Graph Theory, GMM, Outlier Detection
Keywords [sv]
Tillämpad matematik, Klustering, Spektralklustering, grafteori, GMM, anomalidetektering
National Category
Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-264215OAI: oai:DiVA.org:kth-264215DiVA, id: diva2:1375293
External cooperation
Saab
Subject / course
Systems Engineering
Educational program
Master of Science - Aerospace Engineering
Supervisors
Examiners
Available from: 2019-12-04 Created: 2019-12-04 Last updated: 2019-12-04Bibliographically approved

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