A lattice-based approach for mining most generalization association rules

Traditional association rules consist of some redundant information. Some variants based on support and confidence measures such as non-redundant rules and minimal non-redundant rules were thus proposed to reduce the redundant information. In the past, we proposed most generalization association rules (MGARs), which were more compact than (minimal) non-redundant rules in that they considered the condition of equal or higher confidence, instead of only equal confidence. However, the execution time for generating MGARs increased with an increasing number of frequent closed itemsets. Since lattices are an effective data structure widely used in data mining, in this paper, we thus propose a lattice-based approach for fast mining most generalization association rules. Firstly, a new algorithm for building a frequent-closed-itemset lattice is introduced. After that, a theorem on pruning nodes in the lattice for rule generation is derived. Finally, an algorithm for fast mining MGARs from the lattice constructed is developed. The proposed algorithm is tested with several databases and the results show that it is more efficient than mining MGARs directly from frequent closed itemsets.