Outlier Mining in Rule-Based Knowledge Bases
Data
2017
Autorzy
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Wydawnictwo Politechniki Łódzkiej
Lodz University of Technology Press
Lodz University of Technology Press
Abstrakt
This paper introduces an approach to outlier mining in the context of rule-based knowledge bases. Rules in knowledge bases are a very specific type of data representation and it is necessary to analyze them carefully, especially when they differ from each other. The goal of the paper is to analyze the influence of using different similarity measures and clustering methods on the number of outliers discovered during the mining process. The results of the experiments are presented in Section 6 in order to discuss the significance of the analyzed parameters.
Opis
Słowa kluczowe
outlier detection, similarity analysis, clustering, knowledge-based systems, wykrywanie wartości odstających, analiza podobieństwa, grupowanie, systemy oparte na wiedzy
Cytowanie
Nowak-Brzezińska, A. (2017). Outlier Mining in Rule-Based Knowledge Bases. Journal of Applied Computer Science, 25(2), 7-27. https://doi.org/10.34658/jacs.2017.2.7-27