Decreasing Occlusion and Increasing Explanation in Interactive Visual Knowledge Discovery

  • Boris KovalerchukEmail author
  • Abdulrahman Gharawi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10904)


Explanation and occlusion are the major problems for interactive visual knowledge discovery, machine learning and data mining in multidimensional data. This paper proposes a hybrid method that combines the visual and analytical means to deal with these problems. This method, denoted as FSP, uses visualization of n-D data in 2-D, in a set of Shifted Paired Coordinates (SPC). SPCs for n-D data consist of n/2 pairs of Cartesian coordinates, which are shifted relative to each other to avoid their overlap. Each n-D point is represented as a directed graph in SPC. It is shown that the FSP method simplifies the pattern discovery in n-D data, providing the explainable rules in a visual form with a significant decrease of the cognitive load for analysis of n-D data. The computational experiments on real data has shown its efficiency on both training and validation data.


Visual knowledge discovery Visual analytics Visual data mining Occlusion Interactive visualization Shifted Paired Coordinates 


  1. 1.
    Kovalerchuk, B., Grishin, V.: Adjustable general line coordinates for visual knowledge discovery in n-D data. Inf. Vis. (2017).
  2. 2.
    Kovalerchuk, B.: Visual Knowledge Discovery and Machine Learning. Springer, Heidelberg (2018). Scholar
  3. 3.
    Lichman, M.: UCI Machine Learning Repository (2013).
  4. 4.
    Wilinski, A., Kovalerchuk, B.: Visual knowledge discovery and machine learning for investment strategy. Cogn. Syst. Res. 44, 100–114 (2017)CrossRefGoogle Scholar
  5. 5.
    Salama, G.I., Abdelhalim, M., Zeid, M.A.: Breast cancer diagnosis on three different datasets using multi-classifiers. Breast Cancer (WDBC) 32, 2 (2012)Google Scholar
  6. 6.
    Aruna, S., Rajagopalan, D.S., Nandakishore, L.V.: Knowledge based analysis of various statistical tools in detecting breast cancer. Comput. Sci. Inf. Technol. 2, 37–45 (2011)Google Scholar
  7. 7.
    Christobel, A., Sivaprakasam, Y.: An empirical comparison of data mining classification methods. Int. J. Comput. Inf. Syst. 3, 24–28 (2011)Google Scholar
  8. 8.
    Duch, W., Kordos, M.: Multilayer perceptron with numerical gradient. In: ICANN 2003, pp. 106–109 (2003)Google Scholar
  9. 9.
    Hamilton H.J., Shan N., Cercone N., RIAC: A Rule Induction Algorithm Based on Approximate Classification. Computer Science Department, University of Regina (1996)Google Scholar
  10. 10.
    Tu, C.-J., Chuang, L.Y., Yang, C.H.: Feature selection Using PSO-SVM. IAENG Int. J. Comput. Sci. 33(1), 111–116 (2007)Google Scholar
  11. 11.
    Sain, H., Purnami, S.W.: Combine sampling support vector machine for imbalanced data classification. Procedia Comput. Sci. 1(72), 59–66 (2015)CrossRefGoogle Scholar
  12. 12.
    Smiti, A., Elouedi, Z.: Maintaining case based reasoning systems based on soft competence model. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS (LNAI), vol. 8480, pp. 666–677. Springer, Cham (2014). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Central Washington UniversityEllensburgUSA

Personalised recommendations