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Sökning: onr:08hwm5t8xk36zl6d > Data Modeling for O...

  • Abghari, Shahrooz (författare)

Data Modeling for Outlier Detection [Elektronisk resurs]

  • E-bokAvhandlingEngelska2018

Förlag, utgivningsår, omfång ...

  • Publicerad:Karlskrona :Publicerad:Blekinge Tekniska Högskola,Publicerad:2018
  • rordacarrierrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:08hwm5t8xk36zl6d
  • Ogiltigt nummer / annan version:9789172953581
  • http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16580uri
  • urn:nbn:se:bth-16580urn

Kompletterande språkuppgifter

  • Språk:engelska

Ingår i deldatabas

Serie

  • Blekinge Institute of Technology Licentiate Dissertation Series,1650-21401650-2140

Anmärkningar

  • Härtill 4 uppsatser
  • Teknologie licentiatexamen
  • Scalable resource-efficient systems for big data analytics
  • KK-stiftelsen [20140032]
  • Lic.-avh. (sammanfattning), 2018
  • gratis
  • 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.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Lavesson, Niklas (preses)
  • Grahn, Håkan (preses)
  • Boeva, Veselka (preses)
  • Holst, Anders (opponent)
  • Blekinge Tekniska HögskolaFakulteten för datavetenskaper (utgivare)

Sammanhörande titlar

  • Del av/supplement till:channel record
  • Annan version:Annat format9789172953581

Seriebiuppslag

  • Blekinge Institute of Technology Licentiate Dissertation Series,1650-21401650-2140

Internetlänk

Länkade data-URI:er (test)

  • https://id.kb.se/marc/Thesis ()
Hjälp

"Data Modeling for Outlier Detection" finns i 2 utgåvor på 1 språk

Engelska (2)

Engelska (2)

TitelUpplagaÅrMaterialtypHitta
Data Modeling for Outlier Detection [Elektronisk resu...2018 Läs hela
Data modeling for outlier detection / Shahrooz Abghar...2018 3 bibl.
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