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Influence of Similarity Measures for Rules and Clusters on the Efficiency of Knowledge Mining in Rule-Based Knowledge Bases

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Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation (BDAS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 716))

Abstract

In this work the subject of the application of clustering as a knowledge extraction method from real-world data is discussed. The authors analyze the influence of different clustering parameters on the efficiency of the knowledge mining process for rules/rules clusters. In the course of the experiments, nine different objects similarity measures and four clusters similarity measures have been examined in order to verify their impact on the size of the created clusters and the size of their representatives. The experiments have revealed that there is a strong relationship between the parameters used in the clustering process and future efficiency levels of the knowledge mined from such structures: some parameters guarantee to produce shorter/longer representatives of the created rules clusters as well as smaller/greater clusters’ sizes.

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Notes

  1. 1.

    Rules have been extensively used in knowledge representation and reasoning. This is very space efficient as only a relatively small number of facts needs to be stored in the KB and the rest can be derived from the inference rules. Such a natural way of knowledge representation makes the rules easily understood by people not involved in the expert system building.

  2. 2.

    The idea is new but it is based on the authors’ previous research, where the idea of clustering rules was introduced.

  3. 3.

    If both compared objects have the same attribute and this attribute has the same value for both objects then add 1 to a given similarity measure. If otherwise, do nothing. To eliminate one of the problems of SMC, which favours the longest rules, the authors also used Jaccards Index.

  4. 4.

    However the authors see the necesssity to analyze the meaning of methods for creating clusters’ representatives and their influence on the overall efficiency.

  5. 5.

    At this stage some data about sole attributes are also gathered (max. and min. of numerical attributes, the number of times when a categorical attribute had a given value), as they are used in some similarity measures.

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Authors and Affiliations

  1. University of Silesia, ul. Bankowa 12, 40-007, Katowice, Poland

    Agnieszka Nowak-Brzezińska & Tomasz Rybotycki

Authors
  1. Agnieszka Nowak-Brzezińska
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  2. Tomasz Rybotycki
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Corresponding author

Correspondence to Agnieszka Nowak-Brzezińska .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

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Nowak-Brzezińska, A., Rybotycki, T. (2017). Influence of Similarity Measures for Rules and Clusters on the Efficiency of Knowledge Mining in Rule-Based Knowledge Bases. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation. BDAS 2017. Communications in Computer and Information Science, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-319-58274-0_6

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  • DOI: https://doi.org/10.1007/978-3-319-58274-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58273-3

  • Online ISBN: 978-3-319-58274-0

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