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中文摘要：
粒计算是知识表示和数据挖掘的一个重要方法.它模拟人类思考模式,以粒为基本计算单位,以处理大规模复杂数据和信息等建立有效的计算模型为目标.针对具有多粒度标记的序信息系统的知识获取问题,提出了基于序粒度标记结构的粗糙近似.首先,介绍了序标记结构的概念,并在序标记结构的对象集中定义了一个优势关系,同时给出了由优势关系导出的优势标记块,并进一步定义了基于优势关系的集合的序下近似与序上近似和序标记下近似与序标记上近似的概念,给出了近似算子的一些性质.证明了由序标记结构导出的集合的下近似质量与上近似质量是一对对偶的必然性测度与可能性测度.最后,定义了多粒度序标记结构的概念,并讨论了多粒度序标记结构中不同粒度下近似集之间的关系.
英文摘要：
Granular computing, which imitates human being＇s thinking, is an approach for knowledge representation and data mining. Its basic computing unit is called granule, and its objective is to establish effective computation models for dealing with large scale complex data and information. In order to study knowledge acquisition in ordered information systems with multi-granular labels, rough set approximations based on ordered granular labeled structures are explored. The concept of ordered labeled structures is first introduced. A dominance relation on the universe of discourse from an ordered labeled structure is also defined. Dominated labeled blocks determined by the dominance relation are constructed. Ordered lower approximations and ordered upper approximations, as well as ordered labeled lower approximations and ordered labeled upper approximations of sets based on dominance relations, are then proposed. Properties of approximation operators are examined. It is further proved that the qualities of lower and upper approximations of a set derived from an ordered labeled structure are a dual pair of necessity measure and possibility measure. Finally, multi-scale ordered granular labeled structures are defined and relationships among rough approximations with different scales induced from multi-scale ordered granular labeled structures are discussed.
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