Skip to main content
SpringerLink
Log in

Memory Structures and Organization in Case-Based Reasoning

  • Chapter
Case-Based Reasoning on Images and Signals

Part of the book series: Studies in Computational Intelligence ((SCI,volume 73))

Summary

Case-based reasoning (CBR) methodology stems from research on building computational memories capable of analogical reasoning, and require for that purpose specific composition and organization. This main task in CBR has triggered very significant research work and findings, which are summarized and analyzed in this article. In particular, since memory structures and organization rely on declarative knowledge and knowledge representation paradigms, a strong link is set forth in this article between CBR and data mining for the purpose of mining for memory structures and organization. Indeed the richness of data mining methods and algorithms applied to CBR memory building, as presented in this chapter, mirrors the importance of learning memory components and organization mechanisms such as indexing. The article proceeds through an analysis of this link between data mining and CBR, then through an historical perspective referring to the theory of the dynamic memory, and finally develops the two main types of learning related to CBR memories, namely mining for memory structures and mining for memory organization.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aamodt A, Plaza E (1994) Case-Based Reasoning: Foundational Issues, Methodologies Variations, and Systems Approaches. AI Communications, IOS Press, Vol. 7: 1:39–59

    Google Scholar 

  2. Aha DW (1991) Case-Based Learning Algorithms. In: Bareiss R (ed) Proceedings of a Workshop on case-based reasoning (DARPA), Washington, D.C. Morgan Kaufmann, San Mateo, California, pp 147–157

    Google Scholar 

  3. Aha DW (1997) Lazy Learning. Artificial Intelligence Review 11:7–10

    Article  Google Scholar 

  4. Armengol E, Plaza E (1994) Integrating induction in a case-based reasoner. In: Keane M, Haton JP, Manago M (eds) Proceedings of EWCBR 94. Acknosoft Press, Paris, pp 243–251

    Google Scholar 

  5. Auriol E, Manago M, Althoff KD, Wess S, Dittrich S (1994) Integrating Induction and Case-Based Reasoning: Methodological Approach and First Evaluations. In: Keane M, Haton JP, Manago M (eds) Proceedings of EWCBR 94. Acknosoft Press, Paris, pp 145–155

    Google Scholar 

  6. Auriol E, Wess S, Manago M, Althoff KD, Traphöner R (1995) INRECA: A Seamless Integrated System Based on Inductive Inference and Case-Based Reasoning. In: Veloso M, Aamodt A (eds) Proceedings of ICCBR 95. Springer-Verlag, Lecture Notes in Artificial Intelligence 1010, Berlin, Heidelberg, New York, pp 371–380

    Google Scholar 

  7. Bareiss R (1989a) Exemplar-Based Knowledge Acquisition. Academic Press, Inc., San Diego, CA

    MATH  Google Scholar 

  8. Bareiss R (1989b) The Experimental Evaluation of a Case-Based Learning Apprentice. In: Hammond KJ (ed) Proceedings of a Workshop on case-based reasoning (DARPA). Morgan Kaufmann, San Mateo, CA, pp 162–166

    Google Scholar 

  9. Beck HW (1991) Language Acquisition from Cases. In: Bareiss R (ed) Proceedings of a Workshop on case-based reasoning (DARPA), Washington, D.C. Morgan Kaufmann, San Mateo, California, pp 159–169

    Google Scholar 

  10. Bellazzi R, Montani S, Portinale L (1998) Retrieval in a Prototype-Based Case Library: A Case Study in Diabetes Therapy Revision. In: Smyth B, Cunningham P (eds) Proceedings of ECCBR 98. Springer-Verlag, Lecture Notes in Artificial Intelligence 1488, Berlin, Heidelberg, New York, pp 64–75

    Google Scholar 

  11. Bichindaritz I (1990) Alexia: un système de résolution de problèmes par analogie utilisant une mémoire des exemples méta-indexée par un modèle causal appliqué à la détermination de l’étiologie de l’H.T.A. Rapport de stage de DEA, EHEI, Université René Descartes-Paris V

    Google Scholar 

  12. Bichindaritz I (1994) A case-based assistant for clinical psychiatry expertise. In: Proceedings 18th Symposium on Computer Applications in Medical Care. AMIA, Washington DC, pp 673–677

    Google Scholar 

  13. Bichindaritz I (1995a) Case-Based Reasoning and Conceptual Clustering: For a Co-operative Approach. In: Watson I, Fahrir M (eds) Advances in Case-Based Reasoning. Springer-Verlag, Lecture Notes in Artificial Intelligence 1020, Berlin, Heidelberg, New York, pp 91–106

    Google Scholar 

  14. Bichindaritz I (1995b) A case-based reasoner adaptive to several cognitive tasks. In: Veloso M., Aamodt A (eds) Proceedings of ICCBR 95. Springer-Verlag, Lecture Notes in Artificial Intelligence 1010, Berlin, Heidelberg, New York, pp 391–400

    Google Scholar 

  15. Bichindaritz I, Séroussi B (1992) Contraindre l’analogie par la causalité. Technique et sciences informatiques Volume 11, n 4: 69–98

    Google Scholar 

  16. Branting KL (1997) Stratified Case-Based Reasoning in Non-Refinable Abstraction Hierarchies. In: Leake D, Plaza E (eds) Proceedings of ICCBR 97. Springer-Verlag, Lecture Notes in Artificial Intelligence 1266, Berlin, Heidelberg, New York, pp 519–530

    Google Scholar 

  17. Cain T, Pazzani MJ, Silverstein G (1991) Using Domain Knowledge to Influence Similarity Judgment. In: Bareiss R (ed) Proceedings of a Workshop on case-based reasoning (DARPA), Washington, D.C. Morgan Kaufmann, San Mateo, California, pp 191–202

    Google Scholar 

  18. Dìaz-Agudo B, Gonzàlez-Calero P (2001) Classification Based Retrieval Using Formal Concept Analysis. In: Aha DW, Watson I (eds) Proceedings of ICCBR 01. Springer-Verlag, Lecture Notes in Artificial Intelligence 2080, Berlin, Heidelberg, New York, pp 173–188

    Google Scholar 

  19. Dìaz-Agudo B, Gervàz P, Gonzàlez-Calero P (2003) Adaptation Guided Retrieval Based on Formal Concept Analysis. In: Ashley K, Bridge DG (eds) Proceedings of ICCBR 03. Springer-Verlag, Lecture Notes in Artificial Intelligence 2689, Berlin, Heidelberg, New York, pp 131–145

    Google Scholar 

  20. Fisher D (1987) Knowledge acquisition via incremental conceptual clustering. Machine Learning 2:139–172

    Google Scholar 

  21. Gennari JH, Langley P, Fisher D (1989) Models of Incremental Concept Formation. Artificial Intelligence 40:11–61

    Article  Google Scholar 

  22. Hand D, Mannila H, Smyth P (2001) Principles of Data Mining. The MIT Press, Cambridge, Massachusetts

    Google Scholar 

  23. Jarmulak J (1998) Case-Based Classification of Ultrasonic B-Scans: Case-Base Organization and Case Retrieval. In: Smyth B, Cunningham P (eds) Proceedings of ECCBR 98. Springer-Verlag, Lecture Notes in Artificial Intelligence 1488, Berlin, Heidelberg, New York, pp 100–111

    Google Scholar 

  24. Kass A, Leake D, Owens C (1986) SWALE, A Program that Explains. In: Schank RC (ed) Explanation Patterns. Understanding Mechanically and Creatively. Laurence Erlbaum Associates, Publishers, Hillsdale, New Jersey, pp 232–256

    Google Scholar 

  25. King J, Bareiss R (1989) Similarity Assessment and Case-Based Reasoning. In: Hammond KJ (ed) Proceedings of a Workshop on case-based reasoning (DARPA), Pensacola Beach, Florida. Morgan Kaufmann, San Mateo, CA, pp 67–71

    Google Scholar 

  26. Kolodner JL (1993) Case-Based Reasoning. Morgan Kaufmann Publishers, San Mateo, California

    Google Scholar 

  27. Lebowitz M (1982) IPP. In: Schank RC (ed) Dynamic memory: A theory of reminding and learning in computers and people. Cambridge University Press, Cambridge, MA, pp 197–207

    Google Scholar 

  28. Lebowitz M (1983) Generalization From Natural Language Text. Cognitive Science 7:1–40

    Article  Google Scholar 

  29. Lebowitz M (1985) Researcher: An Experimental Intelligent Information System. In: Proceedings IJCAI 85, pp 858–862

    Google Scholar 

  30. Lebowitz M (1986) Concept Learning in a Rich Input Domain: Generalization-Based Memory. In: Michalski RS, Carbonell JG, Mitchell TM (eds) Machine Learning: An Artificial Intelligence Approach, Vol 2. Morgan Kaufmann, Los Altos, CA

    Google Scholar 

  31. Lebowitz M (1987) Experiments with Incremental Concept Formation: UNIMEM. Machine Learning 2:103–138

    Google Scholar 

  32. Lebowitz M (1988) Deferred Commitment in UNIMEM: Waiting to learn. In: Proceedings 5th Machine Learning Conference, Ann Arbor, Michigan. pp 80–86

    Google Scholar 

  33. Michalski RS (1993) Toward a Unified Theory of Learning. In: Buchanan BG, Wilkins DC (eds) Readings in knowledge acquisition and learning, automating the construction and improvement of expert systems. Morgan Kaufmann Publishers, San Mateo, California, pp 7–38

    Google Scholar 

  34. Malek M, Rialle V (1994) A Case-Based Reasoning System Applied to Neuropathy Diagnosis. In: (Keane M, HAton JP, Manago M (eds) Proceedings of EWCBR 94. Acknosoft Press, Paris, pp 329–336

    Google Scholar 

  35. Malek M (1995) A Connectionist Indexing Approach for CBR Systems. In: Veloso M, Aamodt A (eds) Proceedings of ICCBR 95. Springer-Verlag, Lecture Notes in Artificial Intelligence 1010, Berlin, Heidelberg, New York, pp 520–527

    Google Scholar 

  36. Maximini K, Maximini R, Bergmann R (2003) An Investigation of Generalized Cases. In: Ashley KD, Bridge DG (eds) Proceedings of ICCBR 03. Springer-Verlag, Lecture Notes in Artificial Intelligence 2689, Berlin, Heidelberg, New York, pp 261–275

    Google Scholar 

  37. Mitchell TM (1997) Machine Learning. Mc Graw Hill, Boston, Massachusetts

    MATH  Google Scholar 

  38. Mitchell TM, Keller RM, Kedar-Cabelli S (1986) Explanation-based generalization: A unifying view. Machine Learning 1(1): 47–80

    Google Scholar 

  39. Montani S, Portinale L, Bellazzi R, Leornardi G (2004) RHENE: A Case Retrieval System for Hemodialysis Cases with Dynamically Monitored Parameters. In: Funk P, Gonzàlez Calero P (eds) Proceedings of ECCBR 04. Springer-Verlag, Lecture Notes in Artificial Intelligence 3155, Berlin, Heidelberg, New York, pp 659–672

    Google Scholar 

  40. Mougouie B, Bergmann R (2002) Similarity Assessment for Generalized Cases by Optimization Methods. In: Craw S, Preece A (eds) Proceedings of EWCBR 02. Springer-Verlag, Lecture Notes in Artificial Intelligence 2416, Berlin, Heidelberg, New York, pp 249–263

    Google Scholar 

  41. Niloofar A, Jurisica I (2004) Maintaining Case-Based Reasoning Systems: A Machine Learning Approach. In: Funk P, Gonzàlez Calero P (eds) Proceedings of ECCBR 04. Springer-Verlag, Lecture Notes in Artificial Intelligence 3155, Berlin, Heidelberg, New York, pp 17–31

    Google Scholar 

  42. Nilsson M, Funk P (2004) A Case-Based Classification of Respiratory sinus Arrhythmia. In: Funk P, Gonzàlez Calero P (eds) Proceedings of ECCBR 04. Springer-Verlag, Lecture Notes in Artificial Intelligence 3155, Berlin, Heidelberg, New York, pp 673–685

    Google Scholar 

  43. Owens C (1993) Integrating Feature Extraction and Memory Search. In: Kolodner JL (ed) Case-Based Learning. Kluwer Academic Publishers, Boston, pp 117–145

    Google Scholar 

  44. Perner P (1998) Different Learning Strategies in a Case-Based Reasoning System for Image Interpretation. In: Smyth B, Cunningham P (eds) Proceedings of ECCBR 98. Springer-Verlag, Lecture Notes in Artificial Intelligence 1488, Berlin, Heidelberg, New York, pp 251–261

    Google Scholar 

  45. Perner P (2003) Incremenetal Learning of Retrieval Knowledge in a Case-Based Reasoning System. In: Ashley KD, Bridge DG (eds) Proceedings of ICCBR 03. Springer-Verlag, Lecture Notes in Artificial Intelligence 2689, Berlin, Heidelberg, New York, pp 422–436

    Google Scholar 

  46. BW, Bareiss R, Holte RC (1990) Concept Learning and Heuristic Classification in Weak-Theory Domains. Artificial Intelligence, 45:229–263

    Article  Google Scholar 

  47. Portinale L, Torasso P (1995) ADAPTER: An Integrated Diagnostic System Combining Case-Based and Abductive Reasoning. In: Veloso M, Aamodt A (eds) Proceedings of ICCBR 95. Springer-Verlag, Lecture Notes in Artificial Intelligence 1010, Berlin, Heidelberg, New York, pp 277–288

    Google Scholar 

  48. Quinlan JR (1986) Induction of decision trees. Machine Learning 1(1):81–106

    Google Scholar 

  49. Ram A (1993) Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases. In: Kolodner JL (ed) Case-Based Learning. Kluwer Academic Publishers, Boston, pp 7–54

    Google Scholar 

  50. Reategui E, Campbell JA, Borghetti S (1995) Using a Neural Network to Learn General Knowledge in a Case-Based System. In: Veloso M, Aamodt A (eds) Proceedings of ICCBR 95. Springer-Verlag, Lecture Notes in Artificial Intelligence 1010, Berlin, Heidelberg, New York, pp 528–537

    Google Scholar 

  51. Schank RC (1982) Dynamic memory. A theory of reminding and learning in computers and people. Camdridge University Press, Cambridge

    Google Scholar 

  52. Schank RC, Leake DB (1989) Creativity and Learning in a Case-Based Explainer. Articifial Intelligence 40:353–385

    Article  Google Scholar 

  53. Schmidt R, Gierl L (1998) Experiences with Prototype Designs and Retrieval Methods in Medical Case-Based Reasoning Systems. In: Smyth B, Cunningham P (eds) Proceedings of ECCBR 98. Springer-Verlag, Lecture Notes in Artificial Intelligence 1488, Berlin, Heidelberg, New York, pp 370–381

    Google Scholar 

  54. Smyth B, McKenna E (1999) Building Compact Competent Case-Bases. In: Proceedings of ICCBR 99. Springer-Verlag, Berlin, Heidelberg, New York, pp 329–342

    Google Scholar 

  55. Veloso MM, Carbonell JG (1993) Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage and Utilization. In: Kolodner JL (ed) Case-Based Learning. Kluwer Academic Publishers, Norwell, MA, pp 55–84

    Google Scholar 

  56. Utgoff PE (1989) Incremental induction of decision trees. Machine Learning, 4, 2:161–186

    Article  Google Scholar 

  57. West GM, McDonald JR (2003) An SQL-Based Approach to Similarity Assessment within a Relational Database. In: Ashley K, Bridge DG (eds) Proceedings of ICCBR 03. Springer-Verlag, Lecture Notes in Artificial Intelligence 2689, Berlin, Heidelberg, New York, pp 610–621

    Google Scholar 

  58. Wilson DC, Leake DB (2001) Mainting Case-based Reasoners: Dimensions and Directions. Computational Intelligence Journal, Vo. 17, No. 2:196–213

    Google Scholar 

  59. Wiratunga N, Koychev I, Massie S (2004) Feature Selection and Generalisation for Retrieval of Textual Cases. In: Funk P, Gonzàlez Calero P (eds) Proceedings of ECCBR 04. Springer-Verlag, Lecture Notes in Artificial Intelligence 3155, Berlin, Heidelberg, New York, pp 806–820

    Google Scholar 

  60. Yang Q, Cheng H (2003) Case Mining from Large Databases. In: Ashley K, Bridge DG (eds) Proceedings of ICCBR 03. Springer-Verlag, Lecture Notes in Artificial Intelligence 2689, Berlin, Heidelberg, New York, pp 691–702

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Technology, University of Washington, 1900 Commerce street, 98402, Tacoma, USA

    I. Bichindaritz

Authors
  1. I. Bichindaritz
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, IBaI, Arno-Nitzsche-Str.43, 04277, Leipzig, Germany

    Petra Perner

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Bichindaritz, I. (2008). Memory Structures and Organization in Case-Based Reasoning. In: Perner, P. (eds) Case-Based Reasoning on Images and Signals. Studies in Computational Intelligence, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73180-1_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73180-1_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73178-8

  • Online ISBN: 978-3-540-73180-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation