Thereseachofmultiple-leveled Associationruledataminingalgorthm Based On Conception Hierarchy Tree

Data mining is a kind of process that reveals potential useful knowledge from massive, it is an effective way to tackle "Data Rich and Information poor" Association rules are an important aspect of research of DM. The existing research to the association rules only emphasis on the resolving of time efficiency of the algorithm, while ignoring the multiple-leveled performance of the association rules. At the same time, the association rules only represented by raw material, due to the lower supporting degree and it's hard to represent the universal association between data. This dissertation, in the light of the defects of other methods. Sagest an algorithm based on conception hierarchy tree for mining multiple-leveled association rules, based on initiates from a certain intermediate level of the conception hierarchy, matching discover knowledge more efficiency at the same time of supporting degree and reliability of the counting node. The algorithm has shown advantages such as: 1) high efficiency. Comparing with other approaches, it reaches higher performance with less spatial requirements, witch makes it possible to exploit data thoroughly and discover knowledge accurately. 2) Mining association rules are the multiple-leveled association rules, and clean out the gained multiple-leveled association rules, to make the gained rules more accurate.