Abstract
This research empirically investigates the performance of conventional rule interestingness measures and discusses their availability to supporting KDD through system-human interaction in medical domain. We compared the evaluation results by a medical expert and that by selected measures for the rules discovered from a dataset on hepatitis. Recall and ?2 Measure 1 demonstrated the highest performance, and all measures showed different trends under our experimental conditions. These results indicated that some measures can predict really interesting rules at a certain level and that their combinational use in system-human interaction will be useful.
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References
Ohsaki, M., Sato, Y., Yokoi, H., Yamaguchi, T.: A Rule Discovery Support System for Sequential Medical Data, In the Case Study of a Chronic Hepatitis Dataset. In: Proceedings of International Workshop on Active Mining AM 2002 in IEEE International Conference on Data Mining ICDM 2002, pp. 97–102 (2002)
Hilderman, R.J., Hamilton, H.J.: Knowledge Discovery and Measure of Interest. Kluwer Academic Publishers, Dordrecht (2001)
Tan, P.N., Kumar, V., Srivastava, J.: Selecting the Right Interestingness Measure for Association Patterns. In: Proceedings of International Conference on Knowledge Discovery and Data Mining KDD 2002, pp. 32–41 (2002)
Yao, Y.Y., Zhong, N.: An Analysis of Quantitative Measures Associated with Rules. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 479–488. Springer, Heidelberg (1999)
Hepatitis Dataset for Discovery Challenge. In: Web Page of European Conference on Principles and Practice of Knowledge Discovery in Databases PKDD 2002 (2002), http://lisp.vse.cz/challenge/ecmlpkdd2002/index.html
Das, G., King-Ip, L., Heikki, M., Renganathan, G., Smyth, P.: Rule Discovery from Time Series. In: Proceedings of International Conference on Knowledge Discovery and Data Mining KDD 1998, pp. 16–22 (1998)
Lin, J., Keogh, E., Truppel, W.: (Not) Finding Rules in Time Series: A Surprising Result with Implications for Previous and Future Research. In: Proceedings of International Conference on Artificial Intelligence IC-AI 2003, pp. 55–61 (2003)
Ohsaki, M., Sato, Y., Yokoi, H., Yamaguchi, T.: Investigation of Rule Interestingness Measures in Medical Data Mining. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 85–97. Springer, Heidelberg (2005)
Piatetsky-Shapiro, G.: Discovery, Analysis and Presentation of Strong Rules. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 229–248. AAAI/MIT Press (1991)
Smyth, P., Goodman, R.M.: Rule Induction using Information Theory. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 159–176. AAAI/MIT Press (1991)
Hamilton, H.J., Fudger, D.F.: Estimating DBLearn’s Potential for Knowledge Discovery in Databases. Computational Intelligence 11(2), 280–296 (1995)
Dong, G., Li, J.: Interestingness of Discovered Association Rules in Terms of Neighborhood-Based Unexpectedness. In: Wu, X., Kotagiri, R., Korb, K.B. (eds.) PAKDD 1998. LNCS, vol. 1394, pp. 72–86. Springer, Heidelberg (1998)
Gago, P., Bento, C.: A Metric for Selection of the Most Promising Rules. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, pp. 19–27. Springer, Heidelberg (1998)
Morimoto, Y., Fukuda, T., Matsuzawa, H., Tokuyama, T., Yoda, K.: Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases. In: Proceedings of International Conference on Very Large Databases VLDB 1998, pp. 380–391 (1998)
Freitas, A.A.: On Rule Interestingness Measures. Knowledge-Based Systems 12(5-6), 309–315 (1999)
Liu, H., Lu, H., Feng, L., Hussain, F.: Efficient Search of Reliable Exceptions. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 194–203. Springer, Heidelberg (1999)
Jaroszewicz, S., Simovici, D.A.: A General Measure of Rule Interestingness. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 253–265. Springer, Heidelberg (2001)
Zhong, N., Yao, Y.Y., Ohshima, M.: Peculiarity Oriented Multi-Database Mining. IEEE Transaction on Knowledge and Data Engineering 15(4), 952–960 (2003)
Gray, B., Orlowska, M.E.: CCAIIA: Clustering Categorical Attributes into Interesting Association Rules. In: Wu, X., Kotagiri, R., Korb, K.B. (eds.) PAKDD 1998. LNCS, vol. 1394, pp. 132–143. Springer, Heidelberg (1998)
Klementtinen, M., Mannila, H., Ronkainen, P., Toivone, H., Verkamo, A.I.: Finding Interesting Rules from Large Sets of Discovered Association Rules. In: Proceedings of International Conference on Information and Knowledge Management CIKM 1994, pp. 401–407 (1994)
Kamber, M., Shinghal, R.: Evaluating the Interestingness of Characteristic Rules. In: Proceedings of International Conference on Knowledge Discovery and Data Mining KDD 1996, pp. 263–266 (1996)
Liu, B., Hsu, W., Chen, S., Mia, Y.: Analyzing the Subjective Interestingness of Association Rules. Intelligent Systems 15(5), 47–55 (2000)
Liu, B., Hsu, W., Mia, Y.: Identifying Non-Actionable Association Rules. In: Proceedings of International Conference on Knowledge Discovery and Data Mining KDD 2001, pp. 329–334 (2001)
Padmanabhan, B., Tuzhilin, A.: A Belief-Driven Method for Discovering Unexpected Patterns. In: Proceedings of International Conference on Knowledge Discovery and Data Mining KDD 1998, pp. 94–100 (1998)
Sahara, S.: On Incorporating Subjective Interestingness into the Mining Process. In: Proceedings of IEEE International Conference on Data Mining ICDM 2002, pp. 681–684 (2002)
Silberschatz, A., Tuzhilin, A.: On Subjective Measures of Interestingness in Knowledge Discovery. In: Proceedings of International Conference on Knowledge Discovery and Data Mining KDD 1995, pp. 275–281 (1995)
Terano, T., Inada, M.: Data Mining from Clinical Data using Interactive Evolutionary Computation. In: Ghosh, A., Tsutsui, S. (eds.) Advances in Evolutionary Computing, pp. 847–862. Springer, Heidelberg (2003)
Hamilton, H.J., Shan, N., Ziarko, W.: Machine Learning of Credible Classifications. In: Proceedings of Australian Conference on Artificial Intelligence AI 1997, pp. 330–339 (1997)
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Ohsaki, M., Sato, Y., Kitaguchi, S., Yokoi, H., Yamaguchi, T. (2004). Comparison between Objective Interestingness Measures and Real Human Interest in Medical Data Mining. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_110
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DOI: https://doi.org/10.1007/978-3-540-24677-0_110
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