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A Minimum Spanning Tree Clustering Approach for Outlier Detection in Event Sequences
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0002-0535-1761
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
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2018 (English)In: The 17th IEEE International Conference on Machine Learning and Applications Special Session on Machine Learning Algorithms, Systems and Applications, IEEE, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Outlier detection has been studied in many domains. Outliers arise due to different reasons such as mechanical issues, fraudulent behavior, and human error. In this paper, we propose an unsupervised approach for outlier detection in a sequence dataset. The proposed approach combines sequential pattern mining, cluster analysis, and a minimum spanning tree algorithm in order to identify clusters of outliers. Initially, the sequential pattern mining is used to extract frequent sequential patterns. Next, the extracted patterns are clustered into groups of similar patterns. Finally, the minimum spanning tree algorithm is used to find groups of outliers. The proposed approach has been evaluated on two different real datasets, i.e., smart meter data and video session data. The obtained results have shown that our approach can be applied to narrow down the space of events to a set of potential outliers and facilitate domain experts in further analysis and identification of system level issues.

Place, publisher, year, edition, pages
IEEE, 2018.
Keywords [en]
Clustering, Minimum spanning tree, Outlier detection, Sequential pattern mining
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-42988OAI: oai:DiVA.org:hj-42988DiVA, id: diva2:1288956
Conference
IEEE International Conference on Machine Learning and Applications, ICMLA, Orlando
Funder
Knowledge Foundation, 20140032Available from: 2018-10-09 Created: 2019-02-15 Last updated: 2019-08-20Bibliographically approved

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Lavesson, Niklas

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CiteExportLink to record
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  • vancouver
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