Case-Based Reasoning for Candidate List
Extraction in a Marketing Domain
Michael Fagan, Konrad Bloor

BT Laboratories, Martlesham Heath
Ipswich, Suffolk. IP5 3RE. UK
michael.2.fagan@bt.com



Abstract. This paper describes a software tool called CALIBRE (Candidate
Library Retrieval). The tool incorporates case-base reasoning to support the
extraction of candidate lists for targeted marketing campaigns. The tool has
been aimed at users in the marketing domain. This domain is characterised by
very large databases containing many Terabytes of customer related information. 
Large systems such as these require careful management of the queries
being submitted to optimise the use of processing and storage resources. The
CBR approach encourages consistent best practice as well as cutting down on
valuable negotiation time. An early prototype has been built and is currently
used for experimental purposes.
References

[1]	Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, methodological
Variations, and System Approaches. AI Communications 7(1), 39-52, 1994
[2]	Leake, D.: Case-Based Reasoning: Experiences, Lessons, & Future Directions. AAAI
Press, ISBN 0-262-621 l0-X LEACP, 1997
[3]	Piatetsky-Shapiro, G., Frawley, W. J. (eds), Knowledge Discovery in Databases, AAAI
Press, 1991.
[4]	Ribeiro, J. S., Kaufmann, K. A., Kerschberg, L., Knowledge Discovery from Multiple
Databases, in KDD-9S: Proc. of the 1st Intl Conf. on Knowledge Discovery and Data
Mining, U. M. Fayyad, R. Uthurusamy (eds.), AAAI Press, 1995, pp240-245.
[5] Brightware Inc (1996). ARTScript Programming Guide 3, Rules & CBR
[6]	Brown, M. (1993). A Memory model for Case Retrieval by Activation Passing, Department 
of Computer Science, Manchester University.
[7]	Everitt, B.S. (1980), Cluster Analysis, 2d Edition, London: Heineman Educational Books
Ltd
[8]	Fagan, M, Corley S L, CBR for the Reuse of Corporate SQL Knowledge in Advances in
Case-Based Reasoning, EWCBR-98. Springer-Verlag, pp382-39 1.
[9]	Schank, R.C., Abelson, R. (1977) Scipts, Plans; Goals and Understanding: An Inquiry into
Human Knowledge Structures.
[10] SAS, http://www.sas.com/
[11] Richard Stevens, W. (1990). UNIX Network Programming, Prentice-Hall.
[12] Kitano, H., Shibata, A., Shimazu, H., Kajihara, J., & Sato, A. (1992) Building large-scale
and corporate wide case-based systems In, Proceedings of AAAI-92
[13] Netten, B.D., & Vingerhoeds, R.A. (1995) Large-scale fault diagnosis for on-board train
systems In, Case-Based Reasoning Research and Development
[14] Waltz, D. (1996) Large-Scale Applications of CBR In, Advances in Case-Based Reasoning
[15] Schaaf, J.W (1996) Fish and Shrink: A Next Step Towards Efficient Case Retrieval in
Large-Scale Case Bases In, Advances in Case-Based Reasoning
