WEB数据库中低维子空间偏移定位仿真

摘要

在数据库偏移数据定位的优化中,针对传统算法进行WEB数据库中低维子空间偏移数据定位挖掘时,无法判断出WEB数据库中低维子空间偏移数据的位置,出现数据挖掘误差大的问题.提出基于微粒群算法的WEB数据库中低维子空间偏移数据定位挖掘方法.先利用爬虫算法计算低维子空间偏移数据信息覆盖率,设定偏移数据稀疏阈值,融合微粒群算法将该阈值定义偏移数据定位挖掘目标适应值函数,将其适应值与微粒子全局最优位置进行比较,实现偏移数据定位挖掘.仿真结果表明,与传统方法相比,改进算法在定位挖掘精度、时间效率,方面都有很大的提高,并能保证较高的稳定性.%In view of the low dimensional subspace algorithm based on the traditional WEB database migration data location of digging,unable to determine the position of the low dimensional subspace WEB database migration data,appear of the problem of the great error in the data mining.Based on particle swarm algorithm of WEB database in low dimensional subspace migration data to locate mining method.First crawler algorithm is used to calculate the low -dimensional subspace offset data coverage,setting the offset data sparse threshold,the fusion particle swarm algorithm defines the threshold migration data to locate mining target fitness function,the adaptive value compared with the global optimal position particles,locate migration data mining.Simulation experiments show that compared with the traditional method,the improved algorithm in the positioning accuracy and time efficiency,has greatly improved,and higher stability is guaranteed.