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Comparison Analysis of Clustering Quality Criteria Using Inductive Methods of Objective Clustering

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Data Stream Mining & Processing (DSMP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1158))

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Abstract

In this paper, we present the results of the research concerning comparison analysis of both the internal and external clustering quality criteria for clustering various types of datasets using density-based DBSCAN clustering algorithm implemented based on Inductive Methods of Objective Clustering (IMOC). Implementation of the IMOC technique assumes division of the initial dataset into two similar subsets contained the same number of pairwise similar objects at the first step of this procedure implementation. Then, we have executed the data clustering on the obtained subsets concurrently within the range of the appropriate algorithm parameters variation with estimation of various types of clustering quality criteria (internal (IQC) and external (EQC)) at each step of this procedure implementation. The final solution concerning algorithm optimal parameters determination was made based on the maximum values of the complex balance criterion (CBC) which contains both the ICQ and ECQ as the components. The analysis of the simulation results has allowed us to evaluate the effectiveness of both the internal and external clustering quality criteria to determine the optimal parameters of clustering algorithm using various type of data. To our mind, the obtained results can allow us to increase the clustering procedure exactness and to decrease the reproducibility error.

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Authors and Affiliations

  1. Jan Evangelista Purkyně University in Ústí nad Labem, Ústí nad Labem, Czech Republic

    Sergii Babichev & Jiří Škvor

  2. Kherson State University, Kherson, Ukraine

    Sergii Babichev & Aleksander Spivakovskiy

Authors
  1. Sergii Babichev
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  2. Aleksander Spivakovskiy
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  3. Jiří Škvor
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Correspondence to Sergii Babichev .

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Editors and Affiliations

  1. Department of Informatics, Univerzita Jana Evangelisty Purkyně v Ústí nad Labem, Ústí nad Labem, Czech Republic

    Sergii Babichev

  2. GeoGuard, Kharkiv, Ukraine

    Dmytro Peleshko

  3. Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Olena Vynokurova

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Babichev, S., Spivakovskiy, A., Škvor, J. (2020). Comparison Analysis of Clustering Quality Criteria Using Inductive Methods of Objective Clustering. In: Babichev, S., Peleshko, D., Vynokurova, O. (eds) Data Stream Mining & Processing. DSMP 2020. Communications in Computer and Information Science, vol 1158. Springer, Cham. https://doi.org/10.1007/978-3-030-61656-4_10

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  • DOI: https://doi.org/10.1007/978-3-030-61656-4_10

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  • Online ISBN: 978-3-030-61656-4

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