The main aim of the article is to present the modifications of inference algorithms based on information extracted from large rule sets. The article introduces the conception of discovering the knowledge about rules saved in rule bases. It also describes the cluster analysis and decision units conception for this task and presents the optimization of forward and backward inference algorithms as well as selected experimental results.
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