区间集概念格属性约简的组成与结构

区间集是解决部分已知概念、近似不可定义或复杂概念的研究工具。概念格是机器学习、数据挖掘、知识发现和信息检索等领域的一种很有效的数据分析工具。区间集概念格是这两种方法的结合,是对于部分已知概念或不可定义概念信息系统进行机器学习、数据挖掘、知识发现和信息检索的一种有效的数据分析工具。区间集属性约简是揭示区间集概念格本质特征的一种方法。本文揭示了区间集属性约简的组成与结构:两个区间集相对必要属性不能在同一个区间集属性约简中出现;区间集约简与任何一个区间集相对必要属性等价类的交都不空;核心属性和每个区间集相对必要属性等价类中取一个属性组成的集合一定是区间集属性约简。

Composition and structure on attribute reduction of interval-set concept lattices

Interval-set provides a research tool for processing partially known concepts and for approximating undefinable or complex concepts. Concept lattices is a powerful tool for data analysis in machine learning, data mining, knowledge discovery, information retrieval, and so on. Interval-set concept lattices is the product of the combination concept lattices and the interval-set theory,which is a powerful tool for data analysis in machine learning, data mining, knowledge discovery and information retrieval on the information systems of partially known concepts or undefinable concepts. The attribute reduction of interval-set concept lattices is a kind of the method which reveals the elementary character of interval-set concept lattices attribute. This paper reveals the composition and structure of the attribute reduction of interval-set concept lattices. The equivalence relative necessary attributes are not in the same attribute reduction; and the intersection of attribute reduction and any relative necessary attribute equivalence class is nonempty. The set of the core attributes and the relative necessary attributes chosen by taking an attribute from each relative necessary attribute equivalence class must be an attribute reduction.