Outlier Mining in Rule-Based Knowledge Bases

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Miniatura

Data

2017

Tytuł czasopisma

ISSN czasopisma

Tytuł tomu

Wydawca

Wydawnictwo Politechniki Łódzkiej
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