Outlier Detection by Interaction with Domain Experts

• Autorzy: Krasuski A. , Wasilewski P.
• Języki publikacji: EN

Abstrakty

EN

We present a method 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 an element which is significantly different from the remaining data. We follow the position of Professor Andrzej Skowron that effective algorithms in data mining and knowledge discovery in big data should incorporate an interaction with domain experts or/and be domain oriented. 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 analyzed data and experts domain knowledge. In our approach we use the Formal Concept Analysis methods for both generation of the appropriate categories from data and as tools supporting communication with domain experts. We conducted two experiments in finding two types of outliers in which outlier detection was supported by domain experts.

Słowa kluczowe

EN

fire service; formal concept analysis; granular computing; outlier detection

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2013
• Tom: Vol. 127, nr 1-4
• Strony: 529--544
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Krasuski A.

• Section of Computer Science, The Main School of Fire Service, Słowackiego 52/54, 01-629 Warsaw, Poland

autor

Wasilewski P.

• Faculty of Mathematics, Informatics and Mechanics, Warsaw University, Banacha 2, 02-097 Warsaw, Poland

Bibliografia

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