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Methodically Unified Procedures for Outlier Detection, Clustering and Classification

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Book cover Proceedings of the Future Technologies Conference (FTC) 2019 (FTC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1069))

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Abstract

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.

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Notes

  1. 1.

      In the book [30] with the following notation changes: \(W\left( K \right) \mathop \rightarrow \limits ^{into} R\left( K \right) \) and \(U\left( K \right) \mathop \rightarrow \limits ^{into} \mu _2 \left( K \right) \).

  2. 2.

    In the event that such a value does not exist, the presence of one cluster should be recognized and the procedure completed. The same applies to the irrational but formally possible situation m = 1, when set (30) is empty.

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Acknowledgments

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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Authors and Affiliations

  1. Systems Research Institute, Centre of Information Technology for Data Analysis Methods, Polish Academy of Sciences, Kraków, Poland

    Piotr Kulczycki

  2. Faculty of Physics and Applied Computer Science, Division for Information Technology and Systems Research, AGH University of Science and Technology, Kraków, Poland

    Piotr Kulczycki

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  1. Piotr Kulczycki
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Correspondence to Piotr Kulczycki .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Kulczycki, P. (2020). Methodically Unified Procedures for Outlier Detection, Clustering and Classification. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2019. FTC 2019. Advances in Intelligent Systems and Computing, vol 1069. Springer, Cham. https://doi.org/10.1007/978-3-030-32520-6_35

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