燕山大学学报Issue(5)：388-393,402,7.DOI:10.3969/j.issn.1007-791X.2014.05.003
大数据偏序结构生成原理
Generation principle of partial ordered structure towards big data
摘要
Abstract
Formal concept analysis is a powerful tool in data analysis and visualization, and has been applied to data mining, knowledge discovery and many other fields since proposed. However, in the concept lattice, the complex relations between concepts make the lines rather complicated and crossed, especially when dealing with a large-scale formal context. The relation among at-tributes, objects and attribute-object are the essential relations in a formal context. Therefore, under the guidance in the philosophical principle of human being's cognition, the partial ordered structure diagram aiming to delineate the relations among attributes and distinguish distinctive objects is proposed, and construction method is described. Its distinct hierarchy, clear structure, uncrossed lines provide a better visualization. Apart from that, simple computational method of it makes a large potential in allusion to big data. Hence, a novel and efficient tool towards data mining and knowledge discovery of big data is provided by this diagram.关键词
形式背景/偏序结构/大数据/属性偏序结构图/对象偏序结构图Key words
formal context/partial ordered structure/big data/attribute partial ordered structure diagram/object partial ordered structure diagram分类
信息技术与安全科学引用本文复制引用
洪文学,李少雄,张涛,栾景民,刘文远..大数据偏序结构生成原理[J].燕山大学学报,2014,(5):388-393,402,7.基金项目
国家自然科学基金资助项目（61273019，61201111，81273740，81373767）；河北省自然科学基金资助项目 （）
