- title
- The Detection of Outlying Fire Service’s Reports
- subtitle0
- FCA Driven Analytics
- Please use the following URL when quoting:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa-113145
- original_z0
- Cellier, Peggy, Distel, Felix und Ganter, Bernhard (Hrsg.): Contributions to the 11th International Conference on Formal Concept Analysis, 2013, S. 35-50
- Source
- Contributions to the 11th International Conference on Formal Concept Analysis
- Abstract (EN)
- 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.
- Keywords (DE)
- Ausreißer, Ausreißerbestimmung, Formale Begriffsanalyse, Statistik
- Keywords (EN)
- outliers detection, formal concept analysis, fire service
- Classification (DDC)
- 004
- Classification (RVK)
- ST 136
- university_publisher
- Technische Universität Dresden, Dresden
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa-113145
- Qucosa date of publication
- 28.05.2013
- Document type
- in_proceeding
- Document language
- English
- licence
