Abstract
Case-Based Reasoning (CBR) suffers, like the majority of systems, from a large storage requirement and a slow query execution time, especially when dealing with a large case base. As a result, there has been a significant increase in the research area of Case Base Maintenance (CBM).
This paper proposes a case-base maintenance method based on the machine-learning techniques, it is able to maintain the case bases by reducing its size and preserving maximum competence of the system. The main purpose of our method is to apply clustering analysis to a large case base and efficiently build natural clusters of cases which are smaller in size and can easily use simpler maintenance operations. For each cluster we reduce as much as possible, the size of the cluster.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yang, Q., Wu, J.: Keep it simple: A case-base maintenance policy based on clustering and information theory. In: Hamilton, H.J. (ed.) Canadian AI 2000. LNCS (LNAI), vol. 1822, pp. 102–114. Springer, Heidelberg (2000)
Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. Artificial Intelligence Communications 7(1), 39–52 (1994)
Leake, D.B.: Case-Based Reasoning: Experiences, Lessons and Future Directions. MIT Press, Cambridge (1996)
Sun, Z., Finnie, G.: A unified logical model for cbr-based e-commerce systems. International Journal of Intelligent Systems, 1–28 (2005)
Mantaras, L.D., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., Faltings, B., Maher, M.L., Cox, M.T., Forbus, K., Keane, M., Aamodt, A., Watson, I.: Retrieval, reuse, revision and retention in case-based reasoning. Knowl. Eng. Rev. 20(3), 215–240 (2005)
Leake, D.B., Wilson, D.C.: Maintaining case-based reasoners: Dimensions and directions, vol. 17, pp. 196–213 (2001)
Haouchine, M.K., Chebel-Morello, B., Zerhouni, N.: Competence-preserving case-deletion strategy for case-base maintenance. In: Similarity and Knowledge Discovery in Case-Based Reasoning Workshop. 9th European Conference on Case-Based Reasoning, ECCBR 2008, pp. 171–184 (2008)
Asuncion, A., Newman, D.: UCI machine learning repository (2007)
Arshadi, N., Jurisica, I.: Maintaining case-based reasoning systems: A machine learning approach. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 17–31. Springer, Heidelberg (2004)
Cao, G., Shiu, S., Wang, X.: A fuzzy-rough approach for case base maintenance. LNCS, pp. 118–132. Springer, Heidelberg (2001)
Smyth, B., Keane, M.T.: Remembering to forget: A competence-preserving case deletion policy for case-based reasoning systems. In: Proceeding of the 14th International Joint Conference on Artificial Intelligent, pp. 377–382 (1995)
Haouchine, M.K., Chebel-Morello, B., Zerhouni, N.: Auto-increment of expertise for failure diagnostic. In: 13th IFAC Symposium on Information Control Problems in Manufacturing, INCOM 2009, Moscou Russie, pp. 367–372 (2009)
Haouchine, M.K., Chebel-Morello, B., Zerhouni, N.: Methode de suppression de cas pour une maintenance de base de cas. In: 14 eme Atelier de Raisonnement A Partir de Cas, RaPC 2006, France, pp. 39–50 (2006)
Smyth, B.: Case-base maintenance. In: Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE, pp. 507–516 (1998)
Salamó, M., Golobardes, E.: Hybrid deletion policies for case base maintenance. In: Proceedings of FLAIRS 2003, pp. 150–154. AAAI Press, Menlo Park (2003)
Shiu, S.C.K., Yeung, D.S., Sun, C.H., Wang, X.: Transferring case knowledge to adaptation knowledge: An approach for case-base maintenance. Computational Intelligence 17(2), 295–314 (2001)
Bajcsy, P., Ahuja, N.: Location and density-based hierarchical clustering using similarity analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 1011–1015 (1998)
Sander, J., Ester, M., Kriegel, H.P., Xu, X.: Density-based clustering in spatial databases: The algorithm gdbscan and its applications. Data Min. Knowl. Discov. 2(2), 169–194 (1998)
Ester, M., Peter Kriegel, H., Jörg, S., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise, pp. 226–231. AAAI Press, Menlo Park (1996)
Hautamäki, V., Cherednichenko, S., Kärkkäinen, I., Kinnunen, T., Fränti, P.: Improving k-means by outlier removal. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 978–987. Springer, Heidelberg (2005)
Muenchen, A.R., Hilbe, J.M.: R for Stata Users. Statistics and Computing. Springer, Heidelberg (2010)
Bussian, B.M., Härdle, W.: Robust smoothing applied to white noise and single outlier contaminated raman spectra. Appl. Spectrosc. 38(3), 309–313 (1984)
Filzmoser, P., Garrett, R.G., Reimann, C.: Multivariate outlier detection in exploration geochemistry. Computers & Geosciences 31, 579–587 (2005)
Gnanandesikan, R.: Methods for Statistical Data Analysis of Multivariate Observations, 2nd edn. John Wiley & Sons, New York (1997)
Chou, C.H., Kuo, B.H., Chang, F.: The generalized condensed nearest neighbor rule as a data reduction method. In: International Conference on Pattern Recognition, vol. 2, pp. 556–559 (2006)
Li, J., Manry, M.T., Yu, C., Wilson, D.R.: Prototype classifier design with pruning. International Journal on Artificial Intelligence Tools 14(1-2), 261–280 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Smiti, A., Elouedi, Z. (2010). COID: Maintaining Case Method Based on Clustering, Outliers and Internal Detection. In: Lee, R., Ma, J., Bacon, L., Du, W., Petridis, M. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2010. Studies in Computational Intelligence, vol 295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13265-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-13265-0_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13264-3
Online ISBN: 978-3-642-13265-0
eBook Packages: EngineeringEngineering (R0)