A new algorithm for mining fuzzy association rules in the large databases based on ontology

Association rule mining is an active data mining research area. Recent years have witnessed many efforts on discovering fuzzy associations. The key strength of fuzzy association rule mining is its completeness. This strength, however, comes with a major drawback to handle large datasets. It often produces a huge number of candidate itemsets. The huge number of candidate itemsets makes it ineffective for a data mining system to analyze them. To overcome this problem in this study, fuzzy association rule mining system is driven by domain ontology. It describes the use of a concept hierarchy for improving the results of fuzzy association rule mining. Our ontology-based data mining algorithm makes the rules more visual, more interesting and more understandable. At last the paper, the efficiency and advantages of this algorithm has been approved by experimental results.