基于关联分析的计算机软件数据挖掘技术

计算机软件蕴含大量工作信息,有效挖掘软件数据信息之间的内在关联是信息时代对软件应用的潜在要求。针对经典Apriori算法挖掘数据效率低、复杂度高的问题,提出一种改进Apriori算法用于挖掘计算机软件数据的关联规则。为计算机软件算法设置双重支持度阈值,即频繁项集与非频繁项集支持度阈值,快速获得强关联的频繁项集;在此基础上基于映射规则重构事务数据库,压缩数据库规模,减少算法的剪枝操作,降低计算机软件数据关联规则挖掘复杂度。以人力资源类计算机软件数据为例展开关联分析测试,结果显示,该算法挖掘的关联信息与人力资源实际管理情况一致,相比经典Apriori算法其效率有所提升。

Computer Software Data Mining Technology Based on Association Analysis

Computer software contains a lot of work information,it is a potential requirement for software application in the information age to effectively mine the internal relationship between software data and information.Based on the problems of low efficiency and high complexity of classical Apriori algorithm,an improved Apriori algorithm is proposed to mine association rules of computer software data.A double support threshold is set for computer software algorithm,that is,the support threshold of frequent itemsets and non frequent itemsets,so as to quickly obtain the frequent itemsets with strong association.On this basis,the transaction database is reconstructed based on the mapping rules,the size of the database is reduced,the pruning operation of the algorithm is reduced,and the complexity of mining association rules of computer software data is reduced.The results show that the association information mined by this algorithm is consistent with the actual management of human resources,and its efficiency is improved compared with the classic Apriori algorithm.