Return to search

The Detection of Outlying Fire Service’s Reports

We present a methodology for improving the detection of outlying Fire Service’s reports based on domain knowledge and dialogue with Fire & Rescue domain experts. The outlying report is considered as element which is significantly different from the remaining data. Outliers are defined and searched on the basis of domain knowledge and dialogue with experts. We face the problem of reducing high data dimensionality without loosing specificity and real complexity of reported incidents. We solve this problem by introducing a knowledge based generalization level intermediating between analysed data and experts domain knowledge. In the methodology we use the Formal Concept Analysis methods for both generation appropriate categories from data and as tools supporting communication with domain experts. We conducted two experiments in finding two types of outliers in which outliers detection was supported by domain experts.

Identiferoai:union.ndltd.org:DRESDEN/oai:qucosa.de:bsz:14-qucosa-113145
Date28 May 2013
CreatorsKrasuski, Adam, Wasilewski, Piotr
ContributorsTechnische Universität Dresden, Fakultät Mathematik und Naturwissenschaften
PublisherSaechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden
Source SetsHochschulschriftenserver (HSSS) der SLUB Dresden
LanguageEnglish
Detected LanguageEnglish
Typedoc-type:conferenceObject
Formatapplication/pdf
SourceCellier, Peggy, Distel, Felix und Ganter, Bernhard (Hrsg.): Contributions to the 11th International Conference on Formal Concept Analysis, 2013, S. 35-50

Page generated in 0.0019 seconds