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

Conference paper

DOI: 10.1007/978-3-319-58274-0_6

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

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.

Keywords

Rule-based knowledge bases Cluster analysis Similarity measures Clusters visualization Validity index 

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of SilesiaKatowicePoland

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