Visualization of Differences between Rules' Syntactic and Semantic Similarities using Multidimensional Scaling

• Autorzy: Tsumoto S. , Hirano S.
• Języki publikacji: EN

Abstrakty

EN

One of the most important problems with rule induction methods is that it is very difficult for domain experts to check millions of rules generated from large datasets, although the discovery from these rules requires deep interpretation from domain knowledge. Although several solutions have been proposed in the studies on data mining and knowledge discovery, these studies are not focused on similarities between rules obtained. When one rule r1 has reasonable features and the other rule r2 with high similarity to r1 includes unexpected factors, the relations between these rules will become a trigger to the discovery of knowledge. In this paper, we propose a visualization approach to show the similarity relations between rules based on multidimensional scaling, which assign a two-dimensional cartesian coordinate to each data point from the information about similarities between this data and others data. We evaluated this method on two medical data sets, whose experimental results show that knowledge useful for domain experts can be found.

Słowa kluczowe

EN

rule induction; rough sets; multidimensional scaling; visualization

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2007
• Tom: Vol. 78, nr 4
• Strony: 561--573
• Opis fizyczny: bibliogr. 8 poz., tab., wykr.

Twórcy

autor

Tsumoto S.

autor

Hirano S.

• Department of Medical Informatics, Shimane University, School of Medicine, Enya-cho Izumo City, Shimane 693-8501 Japan,

Bibliografia

• [1] Adams, R., Victor, M.: Principles of Neurology 5th Edition, McGraw-Hill, New York, 1993.
• [2] Cox, T., Cox, M.: Multidimensional Scaling, 2nd edition, Chapman & Hall/CRC, Boca Raton, 2000.
• [3] Eckart, C., Young, G.: Approximation of one matrix by another of lower rank, Psychometrika, 1, 1936, 211-218.
• [4] Everitt, B.: Cluster Analysis, 3rd edition, JohnWiley & Son, London, 1996.
• [5] Pawlak, Z.: Rough Sets, Kluwer Academic Publishers, Dordrecht, 1991.
• [6] Skowron, A., Grzymala-Busse, J.: From rough set theory to evidence theory, in: Advances in the Dempster-Shafer Theory of Evidence (R. Yager, M. Fedrizzi, J. Kacprzyk, Eds.), John Wiley & Sons, New York, 1994, 193-236.
• [7] Tsumoto, S., Ziarko, W.: The Application of Rough Sets-Based Data Mining Technique to Differential Diagnosis of Meningoenchepahlitis, Foundations of Intelligent Systems, 9th International Symposium, ISMIS '96, Zakopane, Poland, June 9-13, 1996, Proceedings (Z. W. Ras, M. Michalewicz, Eds.), 1079, Springer, 1996.
• [8] Yao, Y., Zhong, N.: An analysis of quantitative measures associated with rules, Methodologies for Knowledge Discovery and Data Mining, Proceedings of the Third Pacific-Asia Conference on Knowledge Discovery and Data Mining (N. Zhong, L. Zhou, Eds.), 1574, Springer, Berlin, 1999.