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Data Mining can be used for a variety of purposes in Case-Based Reasoning.
Some uses are sketched here, with increasing complexity. Here we assume
that there exists a casebase and in some scenarios also an external database
from which further information could be mined.
- Find features for a case (from casebase) - It might be valuable to
classify the cases in the casebase for use. This might speed up the
computation of similarity metrics in the retrieve step of the
CBR cycle, if cases within one class are believed to
be more similar than to cases outside of the class.
- Find features for a case (from a database) - A database can be
searched to supplement the information given in a case. For instance,
Gibbs sampling can be used to fill in missing features in case-data.
- Find domain knowledge (from a database or a casebase) -
Domain knowledge might be mined from the data in the form of
functions, rules or causal graphs which can later be used by the
Case-Based Reasoner when identifying features and explaining away
unimportant features in the retrieve step, adapting in the
reuse step, or explaining cases in the retain step.
- Construct ``artificial cases'' - one can imagine that it should be possible to construct cases from a database that is not present in a
casebase. This would require a tight integration where the DM algorithm would
search for patterns in the form of cases, which could be evaluated by a
novelty function which gives high values to cases not present in the case-
base. These new cases could be used as an automatic brainstorm on new
problem situations, to provoke discussions on if they are likely to
happen, and on how they can be solved.
Next: Case-Based Reasoning in Data
Up: Integrating DM and CBR
Previous: Integrating DM and CBR
Torgeir Dingsoyr
2/26/1998