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.

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20173

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Data rozpoczęcia: 2016Tematsearch.filters.applied.operator.equals: search.filters.subject.wykrywanie wartości odstających

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Teraz wyświetlane 1 - 3 z 3
  • 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 Detection Using the Multiobjective Genetic Algorithm
    (Wydawnictwo Politechniki Łódzkiej, 2017) Duraj, Agnieszka; Chomątek, Łukasz
    Since almost all datasets may be affected by the presence of anomalies which may skew the interpretation of data, outlier detection has become a crucial element of many datamining applications. Despite the fact that several methods of outlier detection have been proposed in the literature, there is still a need to look for new, more effective ones. This paper presents a new approach to outlier identification based on genetic algorithms. The study evaluates the performance and examines the features of several multiobjective genetic algorithms.
  • 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.