Nowak-Brzezińska, AgnieszkaXięski, Tomasz2021-07-152021-07-152017Nowak-Brzezińska, A., & Xięski, T. (2017). Outlier Mining Using the DBSCAN Algorithm. Journal of Applied Computer Science, 25(2), 53-68. https://doi.org/10.34658/jacs.2017.2.53-681507-0360http://hdl.handle.net/11652/3879https://doi.org/10.34658/jacs.2017.2.53-68This 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.enFair use conditionDla wszystkich w zakresie dozwolonego użytkuoutlier detectionsimilarity analysisclusteringknowledge-based systemswykrywanie wartości odstającychanaliza podobieństwagrupowaniesystemy oparte na wiedzyArtykułLUT LicenseLicencja PŁhttps://doi.org/10.34658/jacs.2017.2.53-6810.34658/jacs.2017.2.53-68