his.sePublications
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Evaluation of cluster based Anomaly detection
University of Skövde, School of Informatics.
2019 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Anomaly detection has been widely researched and used in various application domains such as network intrusion, military, and finance, etc. Anomalies can be defined as an unusual behavior that differs from the expected normal behavior. This thesis focuses on evaluating the performance of different clustering algorithms namely k-Means, DBSCAN, and OPTICS as an anomaly detector. The data is generated using the MixSim package available in R. The algorithms were tested on different cluster overlap and dimensions. Evaluation metrics such as Recall, precision, and F1 Score were used to analyze the performance of clustering algorithms. The results show that DBSCAN performed better than other algorithms when provided low dimensional data with different cluster overlap settings but it did not perform well when provided high dimensional data with different cluster overlap. For high dimensional data k-means performed better compared to DBSCAN and OPTICS with different cluster overlaps

Place, publisher, year, edition, pages
2019. , p. 28
Keywords [en]
Clustering, Unsupervised, Evaluation
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:his:diva-18053OAI: oai:DiVA.org:his-18053DiVA, id: diva2:1382324
Subject / course
Informationsteknologi
Educational program
Data Science - Master’s Programme
Supervisors
Examiners
Available from: 2020-04-08 Created: 2020-01-02 Last updated: 2020-04-08Bibliographically approved

Open Access in DiVA

fulltext(648 kB)8 downloads
File information
File name FULLTEXT01.pdfFile size 648 kBChecksum SHA-512
73b43536ae0d3b6fd783c642e5cb3cddde01a21c283867a699381471a2fd50ed98f6e07e24f847070bbb00b394b2cc2508016d020825c18129c942b25f60e661
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Sreenivasulu, Ajay
By organisation
School of Informatics
Other Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 8 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 30 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf