正负关联规则数据挖掘算法研究

Research on Data Mining Algorithm Based on Positive and Negative Association Rules

目前计算机技术已经由IT时代进入DT时代,要求以提供决策支持信息为目的的数据挖掘技术的快速增长,是从数据库中获取信息、利用信息。对数据挖掘的研究主要是集中在算法的优化与改进上。在总结前人资料的基础上,从另一个角度去研究关联规则——负关联规则,并且使之与传统的关联规则相结合,形成正负关联规则,以使关联规则理论更加完整。对数据挖掘算法里中的关联规则新技术进行了系统、深入、全面、透彻的分析研究。总结,分析和研究了典型的挖掘算法和关联规则的基本思想,并分析了算法之间的差异。客观比较项目之间的联系,相关用于衡量项目集之间的关系。在已有关联规则的情况下,推算出负关联规则的支持度和置信度计算,并对算法的工作原理和实现步骤进行了详细的分析和研究。实验表明,该算法的实验结果提高了关联规则挖掘技术的有效性。

At present,computer technology has entered the DT age from the IT age,which requires the rapid growth of data mining technology for the purpose of providing decision support information,to obtain and utilize information from the database.The research of data mining mainly focuses on the optimization and improvement of algorithm.On the basis of summarizing previous data,we study association rules from another angle-negative association rules,and combine them with traditional association rules to form positive and negative association rules,so as to make association rule theory more complete.The new technology of association rules in data mining algorithm is analyzed systematically,deeply,comprehensively and thoroughly.We summarize,analyze and study the basic ideas of typical mining algorithms and association rules,and analyze the differences between algorithms.Objective comparison of links between projects,correlation is used to measure the relationship between project sets.In the case of existing association rules,the support degree and confidence degree of negative association rules are calculated,and the working principle and implementation steps of the algorithm are analyzed and studied in detail.Experiment shows that the algorithm improves the mining efficiency of association rules mining technology.