Effect of similar behaving attributes in mining of fuzzy association rules in the large databases

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. It often produces a huge number of fuzzy associations. This is particularly true for datasets whose attributes are highly correlated. The huge number of fuzzy associations makes it very difficult for a human user to analyze them. Existing research has shown that most of the discovered rules are actually redundant or insignificant. In this paper, we propose a novel technique to overcome this problem. The approach is effective because experiment results show that the set of produced rules is typically very small. Our solution also reduces the size of average transactions and dataset. Our performance study shows that this solution has a superior performance over the other algorithms.