兴趣模型的构建与Apriori数据挖掘算法优化研究

Optimization of Apriori Algorithm for Mining Interest Model

分析关联规则算法实际过程中参数规律,改进重构Gray和Orlowska兴趣度模型,引入兴趣项、频数阈值、构建兴趣模型,从而能够减少不必要的挖掘扫描操作,提高挖掘的效率,使得挖掘更加有针对性。通过中国石油大学(北京)油气数据挖掘北京市重点实验室提供的标准仿真数据集合对比实验,证明改进后的算法效率更高,适合用于特定兴趣点的二次高效率挖掘。

Parameters of association rules algorithm in the actual process are analyzed,the interest model of Gray and Orlowska reconstruction is improved,interest,frequency threshold are introduced,interest model is built,which can reduce unnecessary mining scanning operation,improve the efficiency of mining,the mining more targeted.The standard simulation data set of the Key Laboratory of China University of Petroleum（Beijing）is used to prove the efficiency of the improved algorithm is higher,which is suitable for the two time efficient mining of specific interest points.