Abstract
Retrieval is often considered the most important task in Case-Based Reasoning (CBR), since it lays the foundation for overall performance of CBR systems. In CBR, a typical retrieval strategy is realized through similarity knowledge encoded in similarity measures. This strategy is often called similarity-based retrieval (SBR). This paper proposes and validates that association analysis techniques can be used to improve SBR. We propose a retrieval strategy USIMSCAR that performs the retrieval task by integrating similarity and association knowledge. We show its reliability, in comparison with several retrieval methods implementing SBR, using datasets from UCI ML Repository.
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
Lopez De Mantaras, R., 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, 215–240 (2005)
Smyth, B., Keane, M.T.: Adaptation-guided retrieval: questioning the similarity assumption in reasoning. Artif. Intell. 102, 249–293 (1998)
Stahl, A.: Learning of knowledge-intensive similarity measures in case-based reasoning. PhD thesis, Technical University of Kaiserslautern (2003)
Dudani, S.A.: The Distance-Weighted k-Nearest-Neighbor Rule. IEEE Transactions on Systems, Man and Cybernetics SMC-6, 325–327 (1976)
Jiang, L., Cai, Z., Wang, D., Jiang, S.: Survey of Improving K-Nearest-Neighbor for Classification. In: FSKD 2007: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 679–683 (2007)
Cunningham, P.: A Taxonomy of Similarity Mechanisms for Case-Based Reasoning. IEEE Trans. on Knowl. and Data Eng. 21, 1532–1543 (2009)
Yusta, S.C.: Different metaheuristic strategies to solve the feature selection problem. Pattern Recogn. Lett. 30, 525–534 (2009)
Wettschereck, D., Aha, D.W.: Weighting features. In: Proceedings of the First International Conference on CBR Research and Development, pp. 347–358 (1995)
Castro, J.L., Navarro, M., Sánchez, J.M., Zurita, J.M.: Loss and gain functions for CBR retrieval. Inf. Sci. 179, 1738–1750 (2009)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994, pp. 487–499 (1994)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings of the 4th KDD, pp. 443–447 (1998)
Nahm, U.Y., Mooney, R.J.: Mining soft-matching association rules. In: Proceedings of CIKM 2002, pp. 681–683 (2002)
Geng, L., Hamilton, H.J.: Interestingness measures for data mining: A survey. ACM Comput. Surv. 38, 9 (2006)
Jurisica, I., Glasgow, J.: Case-Based Classification Using Similarity-Based Retrieval. In: Proceedings of ICTAI (1996)
Witten, I.H., Frank, E.: Data mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Francisco (2000)
Aha, D.W., Kibler, D., Albert, M.K.: Instance-Based Learning Algorithms. Mach. Learn. 6, 37–66 (1991)
Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning. PhD thesis, University of Waikato, Hamilton, New Zealand (1998)
Cleary, J.G., Trigg, L.E.: K*: An Instance-based Learner Using an Entropic Distance Measure. In: Proceedings of the 12th ICML, pp. 108–114 (1995)
Lim, T.S., Loh, W.Y., Shih, Y.S.: A comparison of prediction Accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, 203–229 (2000)
Richard, C.S.: Basic Statistical Analysis. Allyn & Bacon, Boston (2003)
Park, Y.J., Kim, B.C., Chun, S.H.: New knowledge extraction technique using probability for case-based reasoning: application to medical diagnosis. Expert Systems 23, 2–20 (2006)
Hoffmann, A., Khan, A.S.: A new approach for the incremental development of retrieval functions for CBR. Applied Artificial Intelligence 20, 507–542 (2006)
Bergmann, R., Stahl, A.: Similarity measures for object-oriented case representations. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS (LNAI), vol. 1488, pp. 25–36. Springer, Heidelberg (1998)
Ganesan, P., Garcia-Molina, H., Widom, J.: Exploiting hierarchical domain structure to compute similarity. ACM Trans. on Infor. Sys. 21, 64–93 (2003)
Kuba, P., Popelinsky, L.: Mining frequent patterns in object-oriented data (2005)
Pater, S.M., Popescu, D.E.: Market-Basket Problem Solved With Depth First Multi-Level Apriori Mining Algorithm. In: SOFA 2009, 3rd International Workshop on Soft Computing Applications, pp. 133–138 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kang, YB., Krishnaswamy, S., Zaslavsky, A. (2011). A Retrieval Strategy Using the Integrated Knowledge of Similarity and Associations. In: Yu, J.X., Kim, M.H., Unland, R. (eds) Database Systems for Advanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20152-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-20152-3_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20151-6
Online ISBN: 978-3-642-20152-3
eBook Packages: Computer ScienceComputer Science (R0)