hh.sePublications
Change search
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
Incremental classification of process data for anomaly detection based on similarity analysis
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
Volvo Technology, 405 08 Göteborg, Sweden.
Reliability-based Information Systems Engineering, Kagawa University, 761-0396 Kagawa, Japan.
2011 (English)In: EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems : April 11-15, 2011, Paris, France, Piscataway, N.J.: IEEE Press, 2011, 108-115 p.Conference paper, Published paper (Refereed)
Abstract [en]

Performance evaluation and anomaly detection in complex systems are time consuming tasks based on analyzing, similarity analysis and classification of many different data sets from real operations. This paper presents an original computational technology for unsupervised incremental classification of large data sets by using a specially introduced similarity analysis method. First of all the so called compressed data models are obtained from the original large data sets by a newly proposed sequential clustering algorithm. Then the datasets are compared by pairs not directly, but by using their respective compressed data models. The evaluation of the pairs is done by a special similarity analysis method that uses the so called Intelligent Sensors (Agents) and data potentials. Finally a classification decision is generated by using a predefined threshold of similarity. The applicability of the proposed computational scheme for anomaly detection, based on many available large data sets is demonstrated on an example of 18 synthetic data sets. Suggestions for further improvements of the whole computation technology and a better applicability are also discussed in the paper.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE Press, 2011. 108-115 p.
Keyword [en]
anomaly detection, compressed data models, incremental classification, intelligent sensors, sequential clustering, similarity analysis
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:hh:diva-14597DOI: 10.1109/EAIS.2011.5945928Scopus ID: 2-s2.0-80051492287ISBN: 978-142449979-3 OAI: oai:DiVA.org:hh-14597DiVA: diva2:404651
Conference
Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 5th Workshop on Evolving and Adaptive Intelligent Systems, EAIS 2011, Paris, France, 11 - 15 April 2011, Category number CFP1114N-ART, Code85920
Available from: 2011-03-17 Created: 2011-03-17 Last updated: 2013-01-22Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Byttner, StefanSvensson, Magnus
By organisation
Intelligent systems (IS-lab)
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 183 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