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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
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020 z 9789172953581
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024a urn:nbn:se:bth-165802 urn
040 a MimerProde a
041a eng
042 9 EPLK
100a Abghari, Shahrooz4 aut
2451 0a Data Modeling for Outlier Detectionh [Elektronisk resurs]
264 1a Karlskrona :b Blekinge Tekniska Högskola,c 2018
338 b rb o2 rdacarrier2 rdacarrier
490a Blekinge Institute of Technology Licentiate Dissertation Series,x 1650-2140x 1650-2140
500 a Härtill 4 uppsatser
500 a Teknologie licentiatexamen
500 a Scalable resource-efficient systems for big data analytics
500 a KK-stiftelsen [20140032]
502 a Lic.-avh. (sammanfattning), 2018
506a gratis
520 a 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.
650 7a Natural Sciences2 hsv
650 7a Computer and Information Sciences2 hsv
650 7a Computer Sciences2 hsv
650 7a Naturvetenskap2 hsv
650 7a Data- och informationsvetenskap2 hsv
650 7a Datavetenskap (datalogi)2 hsv
653 0a data modeling
653 0a cluster analysis
653 0a stream data
653 0a outlier detection
655 40 https://id.kb.se/marc/Thesis
700a Lavesson, Niklas4 ths
700a Grahn, Håkan4 ths
700a Boeva, Veselka4 ths
700a Holst, Anders4 opn
710a Blekinge Tekniska Högskolab Fakulteten för datavetenskaper4 pbl
772i channel recordw m5z9c2hz4rsn236
7760 8i Annat formatz 9789172953581
830 0a Blekinge Institute of Technology Licentiate Dissertation Series,x 1650-2140x 1650-2140
8564 0u http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16580
8564 0u http://bth.diva-portal.org/smash/get/diva2:1255525/FULLTEXT01
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