Mitra Sushmita, Banka Haider, Pedrycz Witold
Machine Intelligence Unit, Indian Statistical Institute, Kolkata.
IEEE Trans Syst Man Cybern B Cybern. 2006 Aug;36(4):795-805. doi: 10.1109/tsmcb.2005.863371.
In this study, we introduce a novel clustering architecture, in which several subsets of patterns can be processed together with an objective of finding a common structure. The structure revealed at the global level is determined by exchanging prototypes of the subsets of data and by moving prototypes of the corresponding clusters toward each other. Thereby, the required communication links are established at the level of cluster prototypes and partition matrices, without hampering the security concerns. A detailed clustering algorithm is developed by integrating the advantages of both fuzzy sets and rough sets, and a measure of quantitative analysis of the experimental results is provided for synthetic and real-world data.
在本研究中，我们引入了一种新颖的聚类架构，其中几个模式子集可以一起处理，目标是找到一个共同结构。在全局层面揭示的结构是通过交换数据子集的原型以及将相应簇的原型相互移动来确定的。由此，在簇原型和划分矩阵层面建立了所需的通信链路，而不会妨碍安全问题。通过整合模糊集和粗糙集的优点开发了一种详细的聚类算法，并为合成数据和真实世界数据提供了实验结果的定量分析度量。
