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A Minimum Spanning Tree Clustering Approach for Outlier Detection in Event Sequences
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
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2018 (English)In: 2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) / [ed] Wani M.A.,Sayed-Mouchaweh M.,Lughofer E.,Gama J.,Kantardzic M., IEEE, 2018, p. 1123-1130, article id 8614207Conference 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. p. 1123-1130, article id 8614207
Keywords [en]
Clustering, Minimum spanning tree, Outlier detection, Sequential pattern mining
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-17100DOI: 10.1109/ICMLA.2018.00182ISI: 000463034400174ISBN: 9781538668047 (print)OAI: oai:DiVA.org:bth-17100DiVA, id: diva2:1254669
Conference
17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018; Orlando; United States; 17 December 2018 through 20 December
Funder
Knowledge Foundation, 20140032Available from: 2018-10-09 Created: 2018-10-09 Last updated: 2019-06-28Bibliographically approved
In thesis
1. Data Modeling for Outlier Detection
Open this publication in new window or tab >>Data Modeling for Outlier Detection
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis explores the data modeling for outlier detection techniques in three different application domains: maritime surveillance, district heating, and online media and sequence datasets. The proposed models are evaluated and validated under different experimental scenarios, taking into account specific characteristics and setups of the different domains.

Outlier detection has been studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. Outlier detection techniques are often domain-specific. The main challenge in outlier detection relates to modeling the normal behavior in order to identify abnormalities. The choice of model is important, i.e., an incorrect choice of data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and the requirements of the problem domain. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive.

We have studied and applied a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We have shown the importance of data preprocessing as well as feature selection in building suitable methods for data modeling. We have taken advantage of both supervised and unsupervised techniques to create hybrid methods. For example, we have proposed a rule-based outlier detection system based on open data for the maritime surveillance domain. Furthermore, we have combined cluster analysis and regression to identify manual changes in the heating systems at the building level. Sequential pattern mining for identifying contextual and collective outliers in online media data have also been exploited. In addition, we have proposed a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. The proposed models have been shown to be capable of explaining the underlying properties of the detected outliers. This can facilitate domain experts in narrowing down the scope of analysis and understanding the reasons of such anomalous behaviors. We have also investigated the reproducibility of the proposed models in similar application domains.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2018
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 4
Keywords
data modeling, cluster analysis, stream data, outlier detection
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-16580 (URN)978-91-7295-358-1 (ISBN)
Presentation
2018-11-09, Blekinge Tekniska Högskola, Karlskrona, 10:00 (English)
Opponent
Supervisors
Projects
Scalable resource-efficient systems for big data analytics
Funder
Knowledge Foundation, 20140032
Available from: 2018-10-25 Created: 2018-10-12 Last updated: 2018-12-04Bibliographically approved

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Citation style
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