基于绕点旋度修正的粗糙集下挖掘算法  

Data Mining Algorithm Under Rough Set Based on Around Point Rotation Correction

作　　者：于景茹[1] 林海霞[1] 高静[1] 

机构地区：[1]郑州成功财经学院信息工程系,郑州451200

出　　处：《科技通报》2014年第8期113-115,共3页Bulletin of Science and Technology

摘　　要：传统的粗糙集下挖掘算法挖掘能力有限,当海量数据类型多样化时,数据挖掘性能下降。提出一种基于绕点旋度修正的粗糙集下挖掘算法,在数据挖掘时,采用绕点的方法代表系统挖掘中的每个元素点,对于每个绕点,采用旋度评价的方法实现加权修正,通过绕点旋度修正的方法对所有的数据进行融合处理,提取出具体特征,建立数据库,采用迭代方法最大限度的提高挖掘性能。最后采用一组64维度的复杂数据进行测试实验,结果显示,基于绕点旋度修正的数据挖掘能够在大批量多样性数据时实现很好的数据挖掘,具有工程使用价值。In traditional data mining algorithm, the mining capability was limited by the vast diversity of data types, and the data mining performance was declined significantly. So the data mining algorithm under rough set was proposed based on rotation around point correction, the method of digging around the point on behalf of each element in the system of points was used, for each point around, the way of spin evaluation was carried out to achieve a weighted correction, so the rotation around the point correction method for all data integration processing was extracted with the specific characteristics, and the database was established, with the purpose of approaching maximize mining properties. Finally, a group of 64-dimen-sional complex data was used to do test experiment, and the result shows that with rotation around point correction mining method, a good data mining result in large quantities around the diversity of data points was achieved, so it has good appli-cation value for data mining.

关 键 词：绕点 旋度修正 粗糙集 数据挖掘 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]