摘要
三支概念分析是人工智能领域一个非常重要的研究方向,该理论最大的优势是可以同时研究形式背景中对象“共同具有”和“共同不具有”的属性。众所周知,经过属性聚类生成的新形式背景与原形式背景具有较强的联系,同时原三支概念与经过属性聚类得到的新三支概念也存在紧密的内在联系。为此,进行属性聚类下三支概念的对比研究和分析。首先基于属性聚类提出悲观属性聚类、乐观属性聚类以及一般属性聚类的概念,并研究了这三种属性聚类的关系;然后,通过对比聚类过程与三支概念形成的过程,研究了原三支概念与新三支概念的区别,分别从面向对象和面向属性的角度提出两个最低约束指数,探索了属性聚类对三支概念格的影响,进一步丰富了三支概念分析理论,为可视化数据处理领域提供了可行的思路。
Three-way concept analysis is a very important topic in the field of artificial intelligence.The biggest advantage of this theory is that it can study“attributes that are commonly possessed”and“attributes that are commonly not possessed”of the objects in the formal context at the same time.It is well known that the new formal context generated by attribute clustering has a strong connection with the original formal context,and there is a close internal connection between the original three-way concepts and the new three-way concepts obtained by attribute clustering.Therefore,the comparative study and analysis of three-way concepts under attribute clustering were carried out.Firstly,the concepts of pessimistic,optimistic and general attribute clusterings were proposed on the basis of attribute clustering,and the relationship among these three concepts was studied.Moreover,the difference between the original three-way concepts and the new ones was studied by comparing the clustering process with the formal process of three-way concepts.Furthermore,two minimum constraint indexes were put forward from the perspective of object-oriented and attribute-oriented respectively,and the influence of attribute clustering on three-way concept lattice was explored.The above results further enrich the theory of three-way concept analysis and provide feasible ideas for the field of visual data processing.
作者
张晓燕
王佳一
ZHANG Xiaoyan;WANG Jiayi(College of Artificial Intelligence,Southwest University,Chongqing 400715,China)
出处
《计算机应用》
CSCD
北大核心
2023年第5期1336-1341,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(61976245)。
关键词
三支概念
属性聚类
多粒度
最低约束指数
等价类
three-way concept
attribute clustering
multi-granularity
minimum constraint index
equivalence class
