iCaseViz: Learning Case Similarities through Interaction with a Case Base Visualizer

  • Debarun Kar
  • Anand Kumar
  • Sutanu Chakraborti
  • Balaraman Ravindran
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7969)


Since the principal assumption in case-based reasoning (CBR) is that “similar problems have similar solutions”, learning a suitable similarity measure is an important aspect in CBR. However, learning case-case similarities is often a non-trivial task and involves significant amount of domain expertise. Most techniques that arrive at a pertinent similarity measure are often incomprehensible to the domain experts. These techniques also rarely enable the user to provide expert feedback which can then be utilized to develop better similarity measures. Our work attempts to bridge this knowledge gap by developing an iterative and interactive visualization framework called iCaseViz which learns the domain experts’ notion of similarity by utilizing the user feedback. This work is different from similar work in other communities in the sense that it is tailored to cater to the needs of a system built primarily based on the CBR hypothesis. The case base visualizer demonstrated in this paper is also very efficient as it has insignificant delay during real-time user interaction on large case bases. We provide preliminary results on the efficiency of the visualizer and the effectiveness of our similarity learning algorithm on UCI datasets and a real world high dimensional case base.


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  1. 1.
    Khemani, D., Joseph, M.M., Variganti, S.: Case based interpretation of soil chromatograms. In: Althoff, K.-D., Bergmann, R., Minor, M., Hanft, A. (eds.) ECCBR 2008. LNCS (LNAI), vol. 5239, pp. 587–599. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Kar, D., Chakraborti, S., Ravindran, B.: Feature weighting and confidence based prediction for case based reasoning systems. In: Agudo, B.D., Watson, I. (eds.) ICCBR 2012. LNCS, vol. 7466, pp. 211–225. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Aha, D.W.: Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms. Int. J. Man-Mach. Stud. 36(2), 267–287 (1992)CrossRefGoogle Scholar
  4. 4.
    Wettschereck, D.: A study of distance-based machine learning algorithms. Ph.D. dissertation, Department of Computer Science, Oregon State University (1994)Google Scholar
  5. 5.
    Wettschereck, D., Aha, D.W.: Weighting features. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS, vol. 1010, pp. 347–358. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  6. 6.
    Wettschereck, D., Aha, D.W., Mohri, T.: A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artif. Intell. Rev. 11(1-5), 273–314 (1997)CrossRefGoogle Scholar
  7. 7.
    Stahl, A.: Learning feature weights from case order feedback. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 502–516. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  8. 8.
    Cunningham, P.: A taxonomy of similarity mechanisms for case-based reasoning. IEEE Transactions on Knowledge and Data Engineering 21(11), 1532–1543 (2009)CrossRefGoogle Scholar
  9. 9.
    Inselberg, A., Dimsdale, B.: Parallel coordinates: a tool for visualizing multi-dimensional geometry. In: Proceedings of the 1st Conference on Visualization 1990, VIS 1990, pp. 361–378. IEEE Computer Society Press (1990)Google Scholar
  10. 10.
    Massie, S., Craw, S., Wiratunga, N.: Visualisation of case-based reasoning for explanation. In: Proceedings of ECCBR Workshop, Madrid, pp. 135–144 (2004)Google Scholar
  11. 11.
    Falkman, G.: The use of a uniform declarative model in 3D visualisation for case-based reasoning. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 103–117. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Borg, I., Groenen, P.: Modern Multidimensional Scaling: theory and applications. Springer (2005)Google Scholar
  13. 13.
    Broekens, J., Cocx, T., Kosters, W.A.: Object-centered interactive multi-dimensional scaling: Ask the expert. In: Proceedings of the 18th Belgium-Netherlands Conference on Artificial Intelligencem, BNAIC (2006)Google Scholar
  14. 14.
    Buja, A., Swayne, D.F., Littman, M.L., Dean, N., Hofmann, H.: Xgvis: Interactive data visualization with multidimensional scaling. Technical report (2001)Google Scholar
  15. 15.
    desJardins, M., MacGlashan, J., Ferraioli, J.: Interactive visual clustering. In: Proceedings of the 12th International Conference on Intelligent User Interfaces, IUI 2007, pp. 361–364. ACM, New York (2007)CrossRefGoogle Scholar
  16. 16.
    May, T., Bannach, A., Davey, J., Ruppert, T., Kohlhammer, J.: Guiding feature subset selection with an interactive visualization. In: IEEE VAST, pp. 111–120 (2011)Google Scholar
  17. 17.
    Endert, A., Han, C., Maiti, D., House, L., Leman, S., North, C.: Observation-level interaction with statistical models for visual analytics. In: IEEE VAST, pp. 121–130 (2011)Google Scholar
  18. 18.
    Okabe, M., Yamada, S.: An interactive tool for human active learning in constrained clustering. Journal: Emerging Technologies in Web Intelligence 3(1) (2011)Google Scholar
  19. 19.
    Brown, E.T., Liu, J., Brodley, C.E., Chang, R.: Dis-function: Learning distance functions interactively. In: IEEE VAST, pp. 83–92 (2012)Google Scholar
  20. 20.
    Smyth, B., Mullins, M., McKenna, E.: Picture perfect: Visualisation techniques for case-based reasoning. In: ECAI, pp. 65–72 (2000)Google Scholar
  21. 21.
    McArdle, G., Wilson, D.: Visualising case-base usage. In: Workshop Proceedings ICCBR, pp. 105–114 (2003)Google Scholar
  22. 22.
    Namee, B.M., Delany, S.J.: Cbtv: Visualising case bases for similarity measure design and selection. In: Bichindaritz, I., Montani, S. (eds.) ICCBR 2010. LNCS, vol. 6176, pp. 213–227. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  23. 23.
    McKenna, E., Smyth, B.: An interactive visualisation tool for case-based reasoners. Appl. Intell. 14(1), 95–114 (2001)MATHCrossRefGoogle Scholar
  24. 24.
    Chakraborti, S., Cerviño Beresi, U., Wiratunga, N., Massie, S., Lothian, R., Khemani, D.: Visualizing and evaluating complexity of textual case bases. In: Althoff, K.-D., Bergmann, R., Minor, M., Hanft, A. (eds.) ECCBR 2008. LNCS (LNAI), vol. 5239, pp. 104–119. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  25. 25.
    Freyne, J., Smyth, B.: Creating visualizations: A case-based reasoning perspective. In: Coyle, L., Freyne, J. (eds.) AICS 2009. LNCS, vol. 6206, pp. 82–91. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  26. 26.
    CVX Research Inc.: CVX: Matlab software for disciplined convex programming, version 2.0 beta (September 2012)Google Scholar
  27. 27.
    Grant, M., Boyd, S.: Graph implementations for nonsmooth convex programs. In: Blondel, V.D., Boyd, S.P., Kimura, H. (eds.) Recent Advances in Learning and Control. LNCIS, vol. 371, pp. 95–110. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Debarun Kar
    • 1
  • Anand Kumar
    • 1
  • Sutanu Chakraborti
    • 1
  • Balaraman Ravindran
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology MadrasChennaiIndia

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