IMPROVING CASE RETRIEVAL PERFORMANCE THROUGH THE USE OF CLUSTERING TECHNIQUES

Paulo Tomé, Ernesto Costa, Luís Amaral

2008

Abstract

The performance of Case-Based Reasoning (CBR) systems is highly depend on the performance of the retrieval phase. Usually, if the case memory has a large number of cases the system turn to be very slow. Several mechanisms have been proposed in order to prevent a full search of the case memory during the retrieval phase. In this work we propose a clustering technique applied to the memory of cases. But this strategy is applied to an intermediate level of information that defines paths to the cases. Algorithms to the retrieval and retention phase are also presented.

References

  1. Aamodt, A. and Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations and systems approaches. AI-Communications, 7(1):39- 52.
  2. Bogaerts, S. and Leake, D. (2004). Facilitating cbr for incompletely-described cases: Distance metrics for partial problem descriptions. In ECCBR 2004, pages 62-74.
  3. GU, M. and Aamodt, A. (2005). A knowledge-intensive method for conversational cbr. In 6th International Conference on Case-Based Reasoning, pages 296- 311. Springer-Verlag.
  4. Gu, M. and Aamodt, A. (2006). Dialog learning in conversational cbr. In 19th International FLAIRS Conference, pages 358-363, Florida, EUA. AAAI Press.
  5. Kolodner, J. (1993). Case-Based Reasoning. Morgan Kaufmann Publishers.
  6. 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., and Watson, I. (2006). Retrieval, reuse, revison and retention in case-based reasoning. The Knowledge Engineering Review, 20(3):215-240.
  7. Schaaf, J. W. (1996). Fish and shrink. a next step towards efficient case retrieval in large scale case bases. In Smith, I. and Faltings, B., editors, Advances in CaseBased Reasoning, pages 362-376. Springer-Verlag.
  8. Smyth, B. and McKenna, E. (1999). Footprint-based retrieval. In Third International Conference on CaseBased Reasoning, pages 343-357, Munich, Germany. Springer Verlag.
  9. Tan, P. N., Steinbach, M., and Kumar, V. (2006). Introduction to Data Mining. Pearson Education.
  10. Watson, I. (1999). Cbr is a methodology not a technology. Knowledge Based Systems Journal, 12(5-6).
  11. Wilson, D. C. and Leake, D. (2001). Maintaining casebased reasoners: Dimensions and directions. Computational Intelligence, 17(2):196-213.
  12. Wolverton, M. (1994). Retrieving Semantically Distant Analogies. Ph.d thesis, Stanford University.
  13. Yang, Q. and Wu, J. (2000). Keep it simple: A case-base maintenance policy based on clustering and information theory. In Canadian AI 2000, pages 102-114. Springer-Verlag.
Download


Paper Citation


in Harvard Style

Tomé P., Costa E. and Amaral L. (2008). IMPROVING CASE RETRIEVAL PERFORMANCE THROUGH THE USE OF CLUSTERING TECHNIQUES . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-37-1, pages 450-454. DOI: 10.5220/0001687704500454


in Bibtex Style

@conference{iceis08,
author={Paulo Tomé and Ernesto Costa and Luís Amaral},
title={IMPROVING CASE RETRIEVAL PERFORMANCE THROUGH THE USE OF CLUSTERING TECHNIQUES},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2008},
pages={450-454},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001687704500454},
isbn={978-989-8111-37-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - IMPROVING CASE RETRIEVAL PERFORMANCE THROUGH THE USE OF CLUSTERING TECHNIQUES
SN - 978-989-8111-37-1
AU - Tomé P.
AU - Costa E.
AU - Amaral L.
PY - 2008
SP - 450
EP - 454
DO - 10.5220/0001687704500454