不平衡数据挖掘在分布式数据库中的应用

Application of Unbalanced Data Mining in Distributed Database System

为了提高数据库的访问能力和信息检索能力,需要对分布式实时数据库中的不平衡类数据进行有效挖掘,针对当前的Web索引挖掘算法精度较低的问题,提出一种基于语义指向性数据聚类的不平衡类数据挖掘方法,首先进行了分布式实时数据库的存储机制和数据结构分析,然后进行不平衡类数据的时间序列拟合,采用滤波降噪算法进行干扰信息和冗余信息滤除,采用语义特征提取和指向性数据聚类方法实现数据挖掘和聚类处理。最后进行仿真实验分析,结果表明,采用该数据挖掘算法具有较高的精度,抗干扰能力较强,提高了对分布式实时数据库的访问和安全管理能力。

In order to improve the ability to access the database and information retrieval ability, the unbalanced data in distributed real-time database needs to be effectively mined. For the low precision problem of the Web index of the current mining algorithm, a mining method of unbalanced data semantic data is put forward based on clustering analysis and data storage mechanism. First of all, the structure of distributed real-time database is used, then time series of the unbalanced data is fitted, the filtering algorithm is used to filter the interference information and redundant information, and the semantic feature extraction and clustering method is used to realize data mining and clustering of data processing. Finally, the simulation results show that the data mining algorithm has high precision and strong anti-interference ability, and it improves the ability of access and security management of distributed real-time database.