DFR: A New Improved Algorithm for Mining Frequent Itemsets

Efficiency has been concerned in the research of association rules mining. This paper presents an improved method called Direct-Fined-Remove (DFR) algorithm to mine a database consisting of remove and direct steps. When pruning the candidates itemsets, the algorithm eliminate non-frequent subset of candidates in the remove steps. In the direct steps, the algorithm directly generates the frequent itemsets by computing and comparing the frequency of frequent k-itemsts with k in the meantime. The contributions include: (1) proposes an algorithm to raise the probability of obtaining information in scanning database and reduce the potential scale of itemsets. (2) successfully applies the proposed algorithm to the Teaching Evaluation System to determine characteristics of good teaching effect. Experiments show that the probability which grade point of associate professor being more than 90 is 35% the support degree is 40% and the probability which grade point of bachelor being more than 90 is 16% the support degree is 83%.