基于改进布尔约减级数分层的大数据流滞后相关性挖掘方法

Lag Correlation Mining Method Based on Improved Boolean Reduction and Layered Series for Big Data Stream

为了提高大数据流滞后相关性序列挖掘效率,提出基于改进布尔约减级数分层的大数据流滞后相关性挖掘方法.该方法根据原数据流两段序列的序列均值对大数据流序列进行布尔变换,有效降低布尔约减计算开销.通过序列元素转换及还原,缩减序列元素的数目,克服传统算法在滞后相关性计算时需要计算所有数据流序列元素之间滞后相关性的弊端.实验表明,文中方法可有效减少运算时间,在保证精度的同时提高运算效率.

To enhance the efficiency of lag correlation sequences mining for big mining method based on Boolean reduction and layered series is proposed data stream, a lag correlation in this paper. Firstly, by two sequence averages of the original data stream, the big data stream sequence is transformed by the improved Boolean to effectively decrease the Computational cost of Boolean reduction. Secondly, through conversion and reduction of sequence elements, the number of the sequence element is reduced. And the proposed method overcomes the drawback of the traditional algorithm in computing lag correlations of all sequence elements. The experiments show the effective reduction in computational time and obvious improvement in computational accuracy of the proposed method.