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

Conference paper
Part of the Communications in Computer and Information Science book series (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.

Keywords

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

References

  1. 1.
    Bazan, J.G., Szczuka, M.S., Wróblewski, J.: A new version of rough set exploration system. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 397–404. Springer, Heidelberg (2002). doi:10.1007/3-540-45813-1_52 CrossRefGoogle Scholar
  2. 2.
    Berrado, A., Runger, G.: Using metarules to organize and group discovered association rules. Data Min. Knowl. Discov. 14(3), 409–431 (2007)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Chemchem, A., Drias, H., Djenouri, Y.: Multilevel clustering of induction rules for web meta-knowledge. Adv. Intell. Syst. Comput. 206, 43–54 (2013)Google Scholar
  4. 4.
    Dubes, R., Jain, A.: Clustering techniques: the user’s dilemma. Pattern Recogn. 8(4), 247–260 (1976)CrossRefGoogle Scholar
  5. 5.
    Goodall, D.: A new similarity index based on probability. Biometrics 22, 882–907 (1966)CrossRefGoogle Scholar
  6. 6.
    Hashizume, A., Yongguang, B., Du, X., Ishii, N.: Generating representative from clusters of association rules on numeric attributes. In: Liu, J., Cheung, Y., Yin, H. (eds.) IDEAL 2003. LNCS, vol. 2690, pp. 605–613. Springer, Heidelberg (2003). doi:10.1007/978-3-540-45080-1_82 CrossRefGoogle Scholar
  7. 7.
    He, J., Chen, B., Hu, H.J., Harrison, R., Tai, P., Dong, Y., Pan, Y.: Rule clustering and super-rule generation for transmembrane segments prediction. In: IEEE Computational Systems Bioinformatics Conference, Workshops and Poster Abstracts, pp. 224–227 (2005)Google Scholar
  8. 8.
    Latkowski, R., Mikołajczyk, M.: Data decomposition and decision rule joining for classification of data with missing values. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 254–263. Springer, Heidelberg (2004). doi:10.1007/978-3-540-25929-9_30 CrossRefGoogle Scholar
  9. 9.
    Lee, O., Gray, P.: Knowledge base clustering for KBS maintenance. J. Softw. Maint. Evol. 10(6), 395–414 (1998)CrossRefGoogle Scholar
  10. 10.
    Lenty, B., Swamix, A., Widomy, J.: Clustering association rules. Stanford UniversityGoogle Scholar
  11. 11.
    Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
  12. 12.
    Nowak-Brzezińska, A.: Mining rule-based knowledge bases. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2015-2016. CCIS, vol. 613, pp. 94–108. Springer, Cham (2016). doi:10.1007/978-3-319-34099-9_6 CrossRefGoogle Scholar
  13. 13.
    Nowak-Brzezińska, A.: Mining rule-based knowledge bases inspired by rough set theory. Fundam. Inform. 148, 35–50 (2016)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Nowak-Brzezińska, A., Rybotycki, T.: Visualization of medical rule-based knowledge bases. J. Med. Inf. Technol. 24, 91–98 (2015)Google Scholar
  15. 15.
    Prentzas, J., Hatzilygeroudis, I.: Improving efficiency of merging symbolic rules into integrated rules: splitting methods and mergability criteria. Expert Syst. 32(2), 244–260 (2015)CrossRefGoogle Scholar
  16. 16.
    Reynolds, A.P., Richards, G., Rayward-Smith, V.J.: The application of K-medoids and PAM to the clustering of rules. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 173–178. Springer, Heidelberg (2004). doi:10.1007/978-3-540-28651-6_25 CrossRefGoogle Scholar
  17. 17.
    Rybotycki, T.: Wizualizacja struktur hierarchicznych dla regulowych baz wiedzy, Sosnowiec (2015)Google Scholar
  18. 18.
    Shneiderman, B.: Tree visualization with tree-maps: 2-d space-filling approach. Trans. Graphics (TOG) 11, 92–99 (1992). Association for Computing Machinery, New YorkMATHCrossRefGoogle Scholar
  19. 19.
    Wetzel, K.: Pebbles - using circular treemaps to visualize disk usage (2004)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of SilesiaKatowicePoland

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