International Journal of Computer and Communication Technology


Knowledge discovery and databases (KDD) deals with the overall process of discovering useful knowledge from data. Data mining is a particular step in this process by applying specific algorithms for extracting hidden fact in the data. Association rule mining is one of the data mining techniques that generate a large number of rules. Several methods have been proposed in the literature to filter and prune the discovered rules to obtain only interesting rules in order to help the decision-maker in a business process. We propose a new approach to integrate user knowledge using ontologies and rule schemas at the stage of post-mining of association rules. General Terms- Lattice, Post-processing, pruning, itemset





To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.