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A Case Retrieval Approach Using Similarity and Association Knowledge

  • Conference paper
On the Move to Meaningful Internet Systems: OTM 2011 (OTM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7044))

Abstract

Retrieval is often considered the most important phase in Case-Based Reasoning (CBR), since it lays the foundation for overall performance of CBR systems. Retrieval in CBR aims to retrieve relevant cases that can be successfully used for solving a new problem. To realize retrieval, CBR systems typically rely on a strategy that exploits similarity knowledge, and it is called similarity-based retrieval (SBR). In SBR, similarity knowledge approximates the usefulness of cases for solving a new problem. In this paper, we show that association analysis of stored cases can be used to strengthen SBR. We present a new approach for extracting and representing association knowledge from the cases using association rule mining. We propose a novel retrieval strategy USIMSCAR that qualitatively enhances SBR by leveraging both similarity and association knowledge. We demonstrate the significant advantages of using USIMSCAR over SBR through an experimental evaluation using medical datasets.

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References

  1. Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications 7, 39–59 (1994)

    Google Scholar 

  2. Ahn, H., Kim, K.-j.: Global optimization of case-based reasoning for breast cytology diagnosis. Expert Syst. Appl. 36, 724–734 (2009)

    Article  Google Scholar 

  3. Kang, Y.-B., Zaslavsky, A., Krishnaswamy, S., Bartolini, C.: A knowledge-rich similarity measure for improving it incident resolution process. In: Proceedings of the 2010 ACM SAC, pp. 1781–1788. ACM (2010)

    Google Scholar 

  4. Bradley, K., Smyth, B.: Personalized information ordering: a case study in online recruitment. Knowledge-Based Systems 16, 269–275 (2003)

    Article  MATH  Google Scholar 

  5. Nilsson, M., Funk, P., Sollenborn, M.: Complex Measurement Classification in Medical Applications Using a Case-Based Approach. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 63–73. Springer, Heidelberg (2003)

    Google Scholar 

  6. De Mantaras, R.L., 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)

    Article  Google Scholar 

  7. Smyth, B., Keane, M.T.: Adaptation-guided retrieval: questioning the similarity assumption in reasoning. Artif. Intell. 102, 249–293 (1998)

    Article  MATH  Google Scholar 

  8. Stahl, A.: Learning of knowledge-intensive similarity measures in case-based reasoning. PhD thesis. Technical University of Kaiserslautern (2003)

    Google Scholar 

  9. Cercone, N., An, A., Chan, C.: Rule-induction and case-based reasoning: hybrid architectures appear advantageous. IEEE Trans. on Know. and Data Eng. 11, 166–174 (1999)

    Article  Google Scholar 

  10. 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)

    Article  Google Scholar 

  11. Aamodt, A.: Knowledge-intensive case-based reasoning in CREEK. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 1–15. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Hoffmann, A., Khan, A.S.: A new approach for the incremental development of retrieval functions for CBR. Applied Artificial Intelligence 20, 507–542 (2006)

    Article  Google Scholar 

  13. Castro, J.L., Navarro, M., Sánchez, J.M., Zurita, J.M.: Loss and gain functions for CBR retrieval. Inf. Sci. 179, 1738–1750 (2009)

    Article  Google Scholar 

  14. Cunningham, P.: A Taxonomy of Similarity Mechanisms for Case-Based Reasoning. IEEE Trans. on Knowl. and Data Eng. 21, 1532–1543 (2009)

    Article  Google Scholar 

  15. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD 1993, pp. 207–216. ACM (1993)

    Google Scholar 

  16. Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings of the 4th KDD, pp. 443–447 (1998)

    Google Scholar 

  17. Nahm, U.Y., Mooney, R.J.: Mining soft-matching association rules. In: Proceedings of CIKM 2002, pp. 681–683 (2002)

    Google Scholar 

  18. Geng, L., Hamilton, H.J.: Interestingness measures for data mining: A survey. ACM Comput. Surv. 38, 9 (2006)

    Article  Google Scholar 

  19. Hu, T., Sung, S.Y., Xiong, H., Fu, Q.: Discovery of maximum length frequent itemsets. Inf. Sci. 178, 69–87 (2008)

    Article  MathSciNet  Google Scholar 

  20. Jurisica, I., Glasgow, J.: Case-Based Classification Using Similarity-Based Retrieval. In: International Conference on Tools with Artificial Intelligence, p. 410 (1996)

    Google Scholar 

  21. Witten, I.H., Frank, E.: Data mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  22. Richard, C.S.: Basic Statistical Analysis. Allyn & Bacon (2003)

    Google Scholar 

  23. Keen, E.M.: Presenting results of experimental retrieval comparisons. Inf. Process. Manage. 28, 491–502 (1992)

    Article  Google Scholar 

  24. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  25. Park, J.H., Im, K.H., Shin, C.K., Park, S.C.: MBNR: Case-Based Reasoning with Local Feature Weighting by Neural Network: Special Issue: Soft Computing in Case Based Reasoning. Applied Intelligence 21, 265–276 (2004)

    Article  MATH  Google Scholar 

  26. Auriol, E., Wess, S., Manago, M., Althoff, K.-D., Traphöner, R.: INRECA: A Seamlessly Integrated System Based on Inductive Inference and Case-Based Reasoning. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS, vol. 1010, pp. 371–380. Springer, Heidelberg (1995)

    Chapter  Google Scholar 

  27. 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)

    Chapter  Google Scholar 

  28. Kuba, P., Popelinsky, L.: Mining frequent patterns in object-oriented data. Technical Report, Masaryk University, Czech Republic (2005)

    Google Scholar 

  29. 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)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of IT, Monash University, Australia

    Yong-Bin Kang, Shonali Krishnaswamy & Arkady Zaslavsky

  2. ICT Centre, CSIRO, Australia

    Arkady Zaslavsky

Authors
  1. Yong-Bin Kang
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  2. Shonali Krishnaswamy
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  3. Arkady Zaslavsky
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Editor information

Editors and Affiliations

  1. STAR Lab, Vrije Universiteit Brussel (VUB), Bldg G/10, Pleinlaan 2, 1050, Brussel, Belgium

    Robert Meersman

  2. DEBII, Curtin University of Technology, Technology Park, De Laeter Way, 6102, Bentley, WA, Australia

    Tharam Dillon

  3. Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, 28660, Boadilla del Monte, Madrid, Spain

    Pilar Herrero

  4. Smeal College of Business, Pennsylvania State University, University Park, PA 16802, U.S.A.

    Akhil Kumar

  5. Institute of Databases and Information Systems, Ulm University, Germany

    Manfred Reichert

  6. City University of Hong Kong, Hong Kong

    Li Qing

  7. National University of Singapore (NUS), Singapore

    Beng-Chin Ooi

  8. Dipartemento Tecnologie dell’Informazione, Universitá degli Studi di Milano, Via Bramante 65, 26013, Crema, Italy

    Ernesto Damiani

  9. VU Station B #1829, Vanderbilt University, 2015 Terrace Place, TN 37203, Nashville, USA

    Douglas C. Schmidt

  10. Virginia Tech, 24060, Blacksburg, VA, USA

    Jules White

  11. Digital Enterprise Research Institute (DERI), National University of Ireland, IDA Business Park, Lower Dangan, Galway, Ireland

    Manfred Hauswirth

  12. Kno.e.sis Center, Wright State University, Dayton, Ohio, USA

    Pascal Hitzler

  13. IBM India Research Lab, 4, Block C, Institutional Area, Vasant Kunj, 110 070, New Delhi, India

    Mukesh Mohania

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© 2011 Springer-Verlag Berlin Heidelberg

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Kang, YB., Krishnaswamy, S., Zaslavsky, A. (2011). A Case Retrieval Approach Using Similarity and Association Knowledge. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2011. OTM 2011. Lecture Notes in Computer Science, vol 7044. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25109-2_15

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  • DOI: https://doi.org/10.1007/978-3-642-25109-2_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25108-5

  • Online ISBN: 978-3-642-25109-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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