Conclusion

From the discussion in chapter 6, the results in in chapter 5 and the theory in the previous chapter, we can draw the following conclusions:

• The results from the CBRDM tool developed and KATE, differ greatly in the features that are left blank in the ``new case'' under retrieval.
• The number of features and feature values must be reduced in order to produce a Bayesian network with Bayesian Knowledge Discoverer in reasonable time.
• The integration of Bayesian networks and Case-Based Reasoning can lead to better results if the networks represents sound knowledge. This improvement is better when a large number of features are given, and when the given features are sub-nodes in the Bayesian network.