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
BOSE, R. SUBASH CHADRA and SIVAKUMAR, R.
"POST-MINING OF ASSOCIATION RULES USING ONTOLOGIES AND RULE SCHEMAS,"
International Journal of Computer and Communication Technology: Vol. 6:
3, Article 10.
Available at: https://www.interscience.in/ijcct/vol6/iss3/10