面向大规模数据的特征趋势推理算法

提出一种面向大规模数据的特征趋势推理算法. 首先, 采用Hash函数抽取大规模数据样本, 使用Pam聚类算法和并行K means聚类算法对大规模数据样本进行聚类, 获取最佳聚类结果后, 提取大规模数据聚类的动态特征； 其次, 采用基于特征趋势规则的推理算法, 构建大规模数据特征的趋势规则推理模型, 并通过累计趋势规则方法设计趋势规则算法, 推理大规模数据特征趋势, 解决了推理结果误差较大的问题. 实验结果表明, 该算法对大规模数据特征趋势推理的准确率均值为98.10%, 推理速度增长率为50%, 推理耗时最大均值仅为114.25 s, 能快速准确地完成数据特征趋势推理.

Characteristic Trend Reasoning Algorithm for Large Scale Data

The author proposed a characteristic trend reasoning algorithm for large scale data. Firstly, Hash function was used to extract large scale data samples, Pam clustering algorithm and parallel K means clustering algorithm were used to  cluster large scale data samples. After obtaining the best clustering results, the dynamic characteristics of large data clustering were extracted. Secondly, a reasoning algorithm based on characteristic trend rules was used to construct a trend rule reasoning model for large scale data characteristics, and a trend rule algorithm was designed by the method of cumulative trend rule, which could infer the trends of large scale data characteristics, and solved the problem of large errors of reasoning results. The experimental results show that the average accuracy of the proposed algorithm for large-scale data characteristic trend reasoning is 9810%, the growth rate of reasoning speed is 50%, and the maximum average reasoning time consuming is only 11425 s, which can quickly and accurately complete data characteristic trend reasoning.