A density-based quantitative attribute partition algorithm for association rule mining on industrial database

Quantitative attribute partition is an important work of association rule mining, which is widely applied in industrial control at present, and the current partition methods are not suitable for the industrial database, which is generally large, high-dimensional and coupling. The paper proposes a density-based quantitative attribute partition algorithm for industrial database. The proposed algorithm uses an improved density-based clustering algorithm to detect the clusters. The clusters are agglomerated to form the new clusters according to the proximity between clusters and the new clusters are projected into the domains of the quantitative attributes. So the fuzzy sets and the membership functions used for partition are determined. We performed the experiments on a test database and a real industrial database. The experiments results verify the proposed algorithm not only can partition the quantitative attributes of industrial database successfully but also has the higher partition effectiveness.