海量冗余数据干扰下数据库中数据优化检索方法

针对传统方法对数据库中的数据进行检索的过程中,在海量冗余数据干扰时存在无法区分检索数据类别,降低数据检索的效率和精度的问题,提出一种基于特征模糊接近的海量冗余数据干扰下数据库中数据优化检索方法.利用数据模糊集间的接近度表述海量冗余数据干扰下数据库中数据的一致度,结合数据融合技术,对类间数据实现分类处理.利用模糊集算法准确查询分类数据,对分类数据实现二次聚类计算,细分其类边缘,通过加载辨别函数实现数据定位,完成数据检索.实验结果表明:该方法进行数据检索时具有较高的检索效率和精度,且抗干扰能力较强.

Optimization Method of Retrieving Data inthe Database Under the Interference ofLage Redundant Data

In the process of using traditional method to retrieve data in the database, the interference of large redundant data is unable to distinguish when retrieving data category, which reduces the efficiency and accuracy of data retrieval. The paper puts forward an optimization method of retrieving data in the database under the interference of large redundant data based on the characteristics of fussy approaching mass. The method is to use the proximity in the fussy data regions to show the consistency of data in the database under the interference of large redundant data, combine the data fusion technology to classify the indirect data, use fussy set algorithm to query classified data accurately to realise secondary clustering calculation of classified data and segment the edge of class, position the data and complete the data retrieval by loading identification function. The experimental results show that the method for data retrieval has higher retrieval efficiency and accuracy, and strong anti-interference capability.