INFORMATION QUALITY IMPROVEMENT THROUGH ASSOCIATION RULE MINING ALGORITHMS DFCI, DFAPRIORI-CLOSE, EARA, PBAARA, SBAARA.
E. Ramaraj, R. Gokulakrishnan, K. Rameshkumar · 2008
This paper concentrates on the difficulty of the value of the set of revealed association rules. This problem is important since real-life databases capitulate most of the time several thousands of rules with high confidence and propose new algorithms based on closed sets to reduce the mining to bases for exact and estimated rules. Once frequent closed itemsets which constitute a generating set for both frequent itemsets and association rules have been discovered. Proposed algorithms for efficiently generating bases for association rules. A basis is a set of non-redundant rules from which all association rules can be derived, thus it captures all useful information. Moreover, its size is significantly reduced compared with the set of all possible rules because redundant and thus useless rules are discarded. New approach has a twofold advantage on one hand, the user is provided with a smaller set of resulting rules, easier to handle, and information of improved quality. On the other hand, execution times are reduced compared with the discovering of all association rules. Chess dataset used for experiments.