A new association rule classification algorithm using extended concept lattice
Zhai Yu · Journal of Liaoning Technical University · 2015
Associative classification rules generate candidate rules too much for a given dataset. This paper proposed a novel classification rule acquisition method based on the extended-lattice that is composed of the frequent item sets. Firstly, a new and more advantageous lattice structure based on frequent closed itemsets is proposed. Secondly, theorems and properties of the lattice were used to prune the branches of the lattice and reduce redundant rules quickly. The experiment shows that the proposed method is capable of extracting highly useful rules representing key information of the data and in the same time reducing the number of rules significantly. Compared with the typical associative classification algorithms, the proposed method can mine much less number of rules and has higher average classification accuracy.