基于特征模糊贴近的数据库约束挖掘算法

Mining Algorithm of Database Constraints Based on Characteristics Fuzzy Closer

传统的关联规则算法,只考虑了类内的关联性,忽略了类间的相似性特征、高开销的分类过程、耗时的关联过程。提出了数据内间特征模糊贴近分类的数据库约束挖掘算法,其通过数据模糊集间的贴近度描述数据间的一致度,在传统的神经网络挖掘技术中,引入数据融合技术,对类间数据进行分类处理后,对原始挖掘数据的动态特征进行分析获取新的挖掘模型,以在大规模数据库中准确查询目标数据。仿真实验结果表明,算法挖掘稀疏数据集和密集数据集的效率都优于传统的关联规则算法,极大提高了数据库的挖掘效率。

Traditional association rules algorithm only considers the class of close contact, ignores similarity features of the kind, high overhead classification process, time consuming association process. This paper proposed a mining algo- rithm of database constraints based on characteristics fuzzy closer, which describes the consistency between data through the closer between data fuzzy sets, introduces data fusion techniques into the traditional mining technology of neural network, after classifying and processing the data, analyzes the dynamic characteristics of the original mining data and gets new mining model,in order to accurately query target data in the large-scale database. The simulation experi- mental results show that the efficiency for the algorithm to mine the sparse data sets and dense data sets is superior to the traditional association rules algorithm, and it greatly improves the efficiency of database mining.