Journal of Applied Computer Science

Stały URI zbioruhttp://hdl.handle.net/11652/3824

Journal of Applied Computer Science publishes original papers concerned with theory and practice of computer science and innovative computer technology as well as their application in engineering, biomedicine, ecology, socioeconomics and education.

Przeglądaj

Filtry

20172

Ustawienia

Autorsearch.filters.applied.operator.equals: search.filters.author.Nowak-Brzezińska, AgnieszkaTematsearch.filters.applied.operator.equals: search.filters.subject.analiza podobieństwa

Wyniki wyszukiwania

Teraz wyświetlane 1 - 2 z 2
  • Pozycja
    Outlier Mining Using the DBSCAN Algorithm
    (Wydawnictwo Politechniki Łódzkiej, 2017) Nowak-Brzezińska, Agnieszka; Xięski, Tomasz
    This paper introduces an approach to outlier mining in the context of a real-world dataset containing information about the mobile transceivers operation. The goal of the paper is to analyze the influence of using different similarity measures and multiple values of input parameters for the densitybased clustering algorithm on the number of outliers discovered during the mining process. The results of the experiments are presented in section 4 in order to discuss the significance of the analyzed parameters.
  • Pozycja
    Outlier Mining in Rule-Based Knowledge Bases
    (Wydawnictwo Politechniki Łódzkiej, 2017) Nowak-Brzezińska, Agnieszka
    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.