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基于正态灰数的多粒度灰关联分析研究与应用

Research and application for grey relational analysis in multigranularity based on normality grey number.

作者信息

Dai Jin, Liu Xin, Hu Feng

机构信息

College of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

College of Computer Science, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

出版信息

ScientificWorldJournal. 2014 Feb 16;2014:312645. doi: 10.1155/2014/312645. eCollection 2014.

Abstract

Grey theory is an essential uncertain knowledge acquisition method for small sample, poor information. The classic grey theory does not adequately take into account the distribution of data set and lacks the effective methods to analyze and mine big sample in multigranularity. In view of the universality of the normal distribution, the normality grey number is proposed. Then, the corresponding definition and calculation method of the relational degree between the normality grey numbers are constructed. On this basis, the grey relational analytical method in multigranularity is put forward to realize the automatic clustering in the specified granularity without any experience knowledge. Finally, experiments fully prove that it is an effective knowledge acquisition method for big data or multigranularity sample.

摘要

灰色理论是一种针对小样本、信息匮乏的重要不确定性知识获取方法。经典灰色理论没有充分考虑数据集的分布,并且缺乏在多粒度下分析和挖掘大样本的有效方法。鉴于正态分布的普遍性,提出了正态灰色数。然后,构建了正态灰色数之间关联度的相应定义和计算方法。在此基础上,提出了多粒度下的灰色关联分析方法,以在无需任何经验知识的情况下实现指定粒度下的自动聚类。最后,实验充分证明它是一种针对大数据或多粒度样本的有效知识获取方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67f7/3948588/d50b23fa7104/TSWJ2014-312645.001.jpg

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