Multi-level Association Rule Mining Based on Clustering Partition

Abstract

According to the question of the traditional multi-level association rules mining in large data mining in low efficiency and accuracy, based on clustering classification multi-level association rule mining is proposed. The method is combined with the concept of hierarchical concept, the data of the generalization sets processing, and uses SOFM neural network generalization into the database after the transaction, by way of introducing an internal threshold so no need to set the minimum support threshold, to generate the local frequent item sets as global candidates item sets to generate global frequent item sets, thereby enhancing the efficiency of multi-level association rules and accuracy. And by simulating the case shows that the method can not only efficient mining single-layer and cross-layer association rules, but also the association rules is new, easy to understand and meaningful.