Mining Association Rules in Relation of Quantitative Attribute by Coordinating Data

This paper can be divided two parts, the first one researches the characteristic of mining association rules in quantitative attributes relations, and presents a method by coordinating data to transform quantitative attributes data to Boolean attribute for mining and numerical attribute for clustering. The second part, on the base of coordinating data, data is clustered, and then association rules are discovered from the result of clustering, at last association rules are fast adapted to obtain overall result. Through the experiment and the analysis of this algorithm, it is improved that the algorithm is more efficiently than conventional ones.