Interactive Post Mining Association Rules using Cost Complexity Pruning and Ontologies KDD

In data mining, association rule mining is very strong and limited by the huge amount of delivered rules, because of these so many problems facing to implementation. To overcome these drawbacks, several methods were proposed in the literature such as item sets concise, redundancy reduction, and post processing. Based on statistical information by using these methods the extracted rules may not be useful for the user. Thus, it is crucial to help the decision-controller with an efficient post processing step in order to reduce the number of rules. In this paper we have implemented a new interactive approach using cost complexity pruning and filter discovered rules, by using this approach we have to reduce the tree size with minimum number of error of validation set.

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