Abstract
Rule systems have failed to attract much interest in large data analysis problems because they tend to be too simplistic to be useful or consist of too many rules for human interpretation. We recently presented a method that constructs a hierarchical rule system, with only a small number of rules at each level of the hierarchy. Lower levels in this hierarchy focus on outliers or areas of the feature space where only weak evidence for a rule was found in the data. Rules further up, at higher levels of the hierarchy, describe increasingly general and strongly supported aspects of the data. In this paper we show how a connected set of parallel coordinate displays can be used to visually explore this hierarchy of rule systems and allows an intuitive mechanism to zoom in and out of the underlying model.
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References
Salzberg, S.: A nearest hyperrectangle learning method. Machine Learning 6, 251–276 (1991)
Wettschereck, D.: A hybrid nearest-neighbour and nearest-hyperrectangle learning algorithm. In: Proceedings of the European Conference on Machine Learning, pp. 323–335 (1994)
Abe, S., Lan, M.S.: A method for fuzzy rules extraction directly from numerical data and its application to pattern classification. IEEE Transactions on Fuzzy Systems 3, 18–28 (1995)
Higgins, C.M., Goodman, R.M.: Learning fuzzy rule-based neural networks for control. In: Advances in Neural Information Processing Systems, California, vol. 5, pp. 350–357. Morgan Kaufmann, San Francisco (1993)
Simpson, P.K.: Fuzzy min-max neural networks – part 1: Classification. IEEE Transactions on Neural Networks 3, 776–786 (1992)
Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, and Cybernetics 22, 1313–1427 (1992)
Berthold, M.R.: Mixed fuzzy rule formation. International Journal of Approximate Reasoning (IJAR) 32, 67–84 (2003)
Gabriel, T.R., Berthold, M.R.: Constructing hierarchical rule systems. In: R. Berthold, M., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 76–87. Springer, Heidelberg (2003)
Gabriel, T.R., Berthold, M.R.: Influence of fuzzy norms and other heuristics on “mixed fuzzy rule formation”. International Journal of Approximate Reasoning (IJAR) 35, 195–202 (2004)
Berthold, M.R., Hall, L.O.: Visualizing fuzzy points in parallel coordinates. IEEE Transactions on Fuzzy Systems 11, 369–374 (2003)
Fua, Y.H., Ward, M., Rundensteiner, E.A.: Hierarchical parallel coordinates for exploration of large datasets. In: IEEE Conference on Visualization, pp. 43–50 (1999)
Inselberg, A., Dimsdale, B.: Multidimensional lines I: representation. SIAM J. Applied Math. 54, 559–577 (1994)
Inselberg, A., Dimsdale, B.: Multidimensional lines II: proximity and applications. SIAM J. Applied Math. 54, 578–596 (1994)
Inselberg, A.: Multidimensional detective. In: IEEE Symposium on Information Visualization, InfoVis, pp. 100–107. IEEE Press, Los Alamitos (1997)
Chou, S.Y., Lin, S.W., Yeh, C.S.: Cluster identification with parallel coordinates. Pattern Recognition Letters 20, 565–572 (1999)
Berthold, M., Hand, D.J. (eds.): Intelligent Data Analysis: An Introduction, 2nd edn. Springer, Heidelberg (2003)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. In: Annual Eugenics, II, vol. 7, pp. 179–188. John Wiley, Chichester (1950)
Zhang, M., Hall, L., Goldgof, D.: Knowledge-based classification of czcs images and monitoring of red tides off the west florida shelf. In: The 13th International Conference on Pattern Recognition, vol. B, pp. 452–456 (1996)
Zhang, M., Hall, L.O., Goldgof, D.B., Muller-Karger, F.E.: Fuzzy analysis of satellite images to find phytoplankton blooms. In: IEEE International Conference on Systems Man and Cybernetics (1997)
Gordon, H.R., Clark, D.K., Mueller, J.L., Hovis, W.A.: Phytoplankton pigments derived from the nimbus-7 czcs: comparisons with surface measurements. Science 210, 63–66 (1980)
Cheng, T.W., Goldgof, D.B., Hall, L.: Fast fuzzy clustering. Fuzzy Sets and Systems 93, 49–56 (1998)
Zhang, M., Hall, L., Goldgof, D.: Knowledge guided classification of coastal zone color images off the west florida shelf. Technical Report ISL-99-11, University of South Florida, Dept. of CSE, USF, Tampa, FL(1999); Under review in International Journal of Pattern Recognition and AI.
Weislow, O., Kiser, R., Fine, D., Bader, J., Shoemaker, R., Boyd, M.: New soluble formazan assay for HIV-1 cytopathic effects: application to high flux screening of synthetic and natural products for AIDS antiviral activity. Journal National Cancer Institute 81, 577–586 (1989)
Cruciani, G., Crivori, P., Carrupt, P.A., Testa, B.: Molecular fields in quantitative structure-permeation relationships: the VolSurf approach. Journal of Molecular Structure 503, 17–30 (2000)
Michie, D., Spiegelhalter, D.J., Taylor, C.C. (eds.): Machine Learning, Neural and Statistical Classification. Ellis Horwood Limited (1994)
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Gabriel, T.R., Pintilie, A.S., Berthold, M.R. (2005). Exploring Hierarchical Rule Systems in Parallel Coordinates. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds) Advances in Intelligent Data Analysis VI. IDA 2005. Lecture Notes in Computer Science, vol 3646. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552253_10
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DOI: https://doi.org/10.1007/11552253_10
Publisher Name: Springer, Berlin, Heidelberg
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