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Incremental stream clustering and anomaly detection
RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.ORCID iD: 0000-0002-7181-8411
RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
2008 (English)Report (Other academic)
Abstract [en]

This report concerns the "ISC-tool", a tool for classification of patterns and detection of anomalous patterns, where a pattern is a set of values. The tool has a graphical user interface "the anomalo-meter" that shows the degree of anomaly of a pattern and how it is classified. The report describes the user interaction with the tool and the underlying statistical methods used, which basically are Bayesian inference for finding expected or "predictive" distributions for clusters of patterns and using these distributions for classifying and assessing a degree of anomaly to a new pattern. The report also briefly discusses what in general are appropriate methods for clustering and anomaly detection. The project has been supported by SSF via the Butler2 programme.

Place, publisher, year, edition, pages
Swedish Institute of Computer Science , 2008, 1. , 55 p.
Series
SICS Technical Report, ISSN 1100-3154 ; 2008:01
Keyword [en]
Incremental Clustering, Anomaly detection, Bayesian Statistics, Classification
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:ri:diva-22486OAI: oai:DiVA.org:ri-22486DiVA: diva2:1042051
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2017-10-17Bibliographically approved

Open Access in DiVA

fulltext(799 kB)