Autonomous Clustering Using Rough Set Theory
Autonomous Clustering Using Rough Set Theory
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DOI：
10.1007/s11633-008-0090-3
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发表时间：
2008-01-01
影响因子：
4.3
通讯作者：
Kambhampati, Chandra
中科院分区：
文献类型：
--
作者：
Bean, Charlotte;Kambhampati, Chandra
本文提出了一种聚类技术，最大限度地减少了主观人为干预的需要，是基于粗糙集理论（RISK）的元素。该算法是统一的，在其方法来聚类，并利用局部和全局数据属性，以获得聚类解决方案。它可以轻松处理单一类型和混合属性数据集。从三个数据集的单一和混合属性类型的结果被用来说明该技术，并建立其效率。
This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory (RST). The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease. The results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency.