大数据关联关系度量研究综述
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Review for Variable Association Measures in Big Data
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    摘要:

    大数据关联性分析是大数据挖掘的基础,一个好的关联性度量是实施关联分析的关键。本文首先指出大数据时代关联度量面临的挑战和研究现状,从关联关系度量的构造角度出发,对现有的关联关系度量进行整理,归纳总结了这些关联关系的性质和适用条件。在回顾关联度量发展历程的基础上,结合大数据时代关联关系的特点,提出构造关联度量可能满足的条件。最后针对多模态数据关联关系度量的若干问题进行探讨和梳理,从3个角度出发,提出应对多模态数据空间转换的挑战,以引起对该领域更深入的思考与研究工作,从而促进大数据挖掘工作的进展。

    Abstract:

    Association analysis implemented with fantastic association measures is a basis of big data mining, so finding a reasonable measure is a key step for assocization analysis. Firstly, the challenge and research status of association measures are pointed out in the era of big data. From the perspective of the structure of the correlation measure, the exiting measures are systemized, and the properties and applicable corditions are summarized, respectively. Secondly, based on the development of correlation measures and the challanges of big data era, some conditions for meeting association measure are put forward to respond to meetting association measure challeges. Finally, some correlation measures in multi-modal data analysis are discussed and combed, and some ideas are provided to deal with the space conversion from three different angles, which attract more in depth thinking and research, therefore promoting the progress on big data mining.

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钱宇华 成红红梁新彦 王 建新.大数据关联关系度量研究综述[J].数据采集与处理,2015,30(6):1147-1159

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  • 在线发布日期: 2015-12-24