面向大数据的多维粒矩阵关联分析及应用

doi:10.11896/j.issn.1002-137X.2017.11A.086

摘要/Abstract

摘要： 当前日益增长的大数据备受青睐,大数据的核心是数据分析。然而聚焦大数据的动态、多维特性,传统数据分析方法难以获取可靠且准确的分析结果,数据分析方法面临着重要的发展机遇和严峻的挑战。对动态大数据的多维关联性分析问题进行研究和探讨,以动态大数据为研究对象,以粒计算(Granular Computing,GrC)理论为研究基础,提出粒矩阵思想,研究构建面向动态大数据的粒矩阵方法,分析粒矩阵的逻辑约简运算,确定了基于粒矩阵的动态大数据多维关联性分析模型。本文旨在为高效利用动态大数据进行多维关联性分析和揭示数据隐含的客观规律提供科学依据,对大数据的可持续发展也具有重要意义。

关键词: 大数据,粒计算,多维关联分析

Abstract: The ever increasing big data is acclaimed,and the key point of big data is data analysis.However focusing on the big data with dynamic and multiple-dimensional characteristic,it is difficult for traditional data analysis methods to obtain reliable and accurate analytical results.Therefore there is an important opportunity and a great challenge for the data analysis methods to be developed.It aims to make an important research and investigation of the multiple correlation analysis for dynamic big data.The research object is dynamic big data,and the research is based on the theory of granular computing (GrC).We got the theoretical thought of granular matrix completely new in this paper.It was expected to reveal the multiple correlation analysis for dynamic big data.On one hand the achievements of this research would provide a scientific basis for multiple correlation analysis and revelation of the objective law in big data area.On the other hand it is also an important implication for sustainable development of big data.

Key words: Big data,Granular computing,Multiple correlation analysis

吴珺,王春枝. 面向大数据的多维粒矩阵关联分析及应用[J]. 计算机科学, 2017, 44(Z11): 407-410. https://doi.org/10.11896/j.issn.1002-137X.2017.11A.086

WU Jun and WANG Chun-zhi. Multiple Correlation Analysis and Application of Granular Matrix Based on Big Data[J]. Computer Science, 2017, 44(Z11): 407-410. https://doi.org/10.11896/j.issn.1002-137X.2017.11A.086

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