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Two approaches for using CBR in DM are sketched, the first is to use the
CBR environment as the environment for the KDD process for the data mining
algorithm. The second is to provide the DM with information which is required
in order to produce good results.
- DM search is the case - Data Mining is only one part
of the KDD process which can involve accessing several files, cleaning
data, and interpreting results. The data mining search may also be
time-consuming. The information about the search results and the whole
KDD process might be stored in a case so that extra time will not be
spent on mining the same information more than once. A need for this approach
is signaled after discussions on the CRISP-DM project to develop a
standard process model for data mining, which states: ``a standard
methodology for data mining must provide a framework for capturing and
re-using experiences, and for guiding data mining projects at different
levels of skills''.
- CBR provides info - CBR can be used in providing some background
knowledge about features in a database, for instance, the weight of features
for a classifier can be learnt from the CBR tool. In a Bayesian network,
the structure of the network might be set up by the CBR tool (model
construction), using its ``expert knowledge'' and the parameters learned
using DM algorithms. CBR can also be used to provide utility, validity and
novelty functions for the DM algorithm from the domain that the CBR tool is
working in (model evaluation).
Next: Integration in Existing Systems
Up: Integrating DM and CBR
Previous: Data Mining in Case-Based
Torgeir Dingsoyr
2/26/1998