Methodically Unified Procedures for Outlier Detection, Clustering and Classification

  • Piotr KulczyckiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1069)


In the practice of data analysis some problems for many-sided researches are caused by the methodological variety of specific algorithms, often leading to laborious interpretations and time-consuming studies. This paper presents the concept of methodically unified procedures, based on kernel estimators, for three fundamental tasks: outlier detection, clustering, and classification. Their clear interpretation facilitates the applications and potential individual modifications. The investigated procedures are distribution-free, enabling analysis and exploration of data with any distributions, also when elements are grouped in several separated parts. The results obtained depend not only on the values of particular attributes, but above all on the complex relationships between them.


Outlier detection Clustering Classification Distribution free methods Kernel estimators Numerical algorithm 



I would like to express my gratitude to my close associates – former Ph.D.-students – Małgorzata Charytanowicz, D.Sc., Karina Daniel, Ph.D., Piotr A. Kowalski, D.Sc., Damian Kruszewski, Ph.D., Szymon Łukasik, Ph.D., with whom the research summarized in this paper was conducted.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Systems Research Institute, Centre of Information Technology for Data Analysis MethodsPolish Academy of SciencesKrakówPoland
  2. 2.Faculty of Physics and Applied Computer Science, Division for Information Technology and Systems ResearchAGH University of Science and TechnologyKrakówPoland

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