基于不可分辨性和不可分辨程度的聚类

R. B. F. Hakim, Subanar, E. Winarko
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引用次数: 4

摘要

经典粗糙集的核心概念是基于不可分辨和可分辨的概念对对象的相似性和差异性进行聚类。本文提出了一种基于不可分辨性及其不可分辨程度相结合的数据聚类方法。不可识别级别量化了信息系统中对象对之间的不可识别性。本文的研究结果表明,不可分辨的双重概念及其不可分辨程度在聚类信息系统中起着重要的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clustering based-on indiscernibility and indiscernibility level
The core concept of classical rough sets are clustering similarities and dissimilarities of objects based on the notions of indiscernibility and discernibility. In this paper, we present a new method of clustering data based on the combination of indiscernibility and its indiscernibility level. The indiscernibility level quantifies the indiscernibility of pairs of objects among other objects in information systems. The result of this paper show the dual notions of indiscernibility and its indiscernibility level play an important role in clustering information systems.
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