Abstract To enhance the efficiency of lag correlation sequences mining for big data stream, a lag correlation mining method based on Boolean reduction and layered series is proposed 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.
About author:: 任永功,男,1972年生,博士,教授,主要研究方向为数据库技术、数据挖掘、智能信息计算等.E-mail: ryg@lnnu.edu.cn. (REN Yonggong, born in 1972, Ph.D., professor. His research interests include database technology, data mining and intelligent information calculation.) 钱海振,男,1986年生,硕士研究生,主要研究方向为数据挖掘.E-mail:243127387@qq.com. (QIAN Haizhen, born in 1986, master student. His research interests include data mining.) 郎泓钰,男,1989年生,硕士研究生,主要研究方向为数据挖掘.E-mail:15998416927@163.com. (LANG Hongyu, born in 1989, master student. His research interests include data mining.)
REN Yonggong,QIAN Haizhen,LANG Hongyu. Lag Correlation Mining Method Based on Improved Boolean Reduction and Layered Series for Big Data Stream[J]. , 2016, 29(5): 455-463.
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