In this paper, we first introduce the concepts of knowledge closeness and knowledge distance for measuring the sameness and the difference among knowledge in an information system, respectively. The relationship betwe...In this paper, we first introduce the concepts of knowledge closeness and knowledge distance for measuring the sameness and the difference among knowledge in an information system, respectively. The relationship between these two concepts is a strictly mutual complement relation. We then investigaie some important properties of knowledge distance and perform experimental analyses on two public data sets, which show the presented measure appears to be well suited to characterize the nature of knowledge in an information system. Finally, we establish the relationship between the knowledge distance and knowledge granulation, which shows that two variants of the knowledge distance can also be used to construct the knowledge granulation. These results will be helpful for studying uncertainty in information systems.展开更多
Data granulation is a good tool of decision making in various types of real life applications. The basic ideas of data granulation have appeared in many fields, such as interval analysis, quantization, rough set theor...Data granulation is a good tool of decision making in various types of real life applications. The basic ideas of data granulation have appeared in many fields, such as interval analysis, quantization, rough set theory, Dempster-Shafer theory of belief functions, divide and conquer, cluster analysis, machine learning, databases, information retrieval, and many others. In this paper, we initiate some new topological tools for data granulation using rough set approximations. Moreover, we define some topological measures of data granulation in topological I formation systems. Topological generalizations using δβ-open sets and their applications of information granulation are developed.展开更多
为降低不完备决策表求核算法的时间复杂度,本文构造了粒度二进制的差别矩阵。然后定义属性重要性及相应的核,由此设计了一个基于不完备决策表的粒度二进制差别矩阵的求核算法,并分析新算法的时间复杂度,其时间复杂度降为max{O(C U Upos)...为降低不完备决策表求核算法的时间复杂度,本文构造了粒度二进制的差别矩阵。然后定义属性重要性及相应的核,由此设计了一个基于不完备决策表的粒度二进制差别矩阵的求核算法,并分析新算法的时间复杂度,其时间复杂度降为max{O(C U Upos),O(K C U)},优于同类算法的时间复杂度,最后用实例说明了该算法的有效性。展开更多
基金the National Natural Science Foundation of China under Grant Numbers 60773133,70471003,and 60573074the High Technology Research and Development Program of China under Grant No.2007AA01Z165+1 种基金the Foundation of Doctoral Program Research of the Ministry of Education of China under Grant No.20050108004Key Project of Science and Technology Research of the Ministry of Education of China.
文摘In this paper, we first introduce the concepts of knowledge closeness and knowledge distance for measuring the sameness and the difference among knowledge in an information system, respectively. The relationship between these two concepts is a strictly mutual complement relation. We then investigaie some important properties of knowledge distance and perform experimental analyses on two public data sets, which show the presented measure appears to be well suited to characterize the nature of knowledge in an information system. Finally, we establish the relationship between the knowledge distance and knowledge granulation, which shows that two variants of the knowledge distance can also be used to construct the knowledge granulation. These results will be helpful for studying uncertainty in information systems.
文摘Data granulation is a good tool of decision making in various types of real life applications. The basic ideas of data granulation have appeared in many fields, such as interval analysis, quantization, rough set theory, Dempster-Shafer theory of belief functions, divide and conquer, cluster analysis, machine learning, databases, information retrieval, and many others. In this paper, we initiate some new topological tools for data granulation using rough set approximations. Moreover, we define some topological measures of data granulation in topological I formation systems. Topological generalizations using δβ-open sets and their applications of information granulation are developed.
文摘为降低不完备决策表求核算法的时间复杂度,本文构造了粒度二进制的差别矩阵。然后定义属性重要性及相应的核,由此设计了一个基于不完备决策表的粒度二进制差别矩阵的求核算法,并分析新算法的时间复杂度,其时间复杂度降为max{O(C U Upos),O(K C U)},优于同类算法的时间复杂度,最后用实例说明了该算法的有效性。
