摘要
提出了一种新的聚类评价方法,该方法以聚类的代表点表示法为基础,在经典方法上做出了改进。首先将聚类结果对应于模态逻辑中Kripke结构;然后利用模态逻辑中语法与语义之间的对应性选取了相应的公理系统。通过公式之间的蕴涵关系,选择一组极少的数据点来表示聚类结果的各种信息,形成聚类的模态代表点。在此基础上,给出了相应的聚类评价方法。这种方法除了可以评价聚类结果的优劣,还可以分析出簇的形态。实验表明,与一些常用聚类评价指标相比,这种评价方法更具通用性。
A new clustering validity index based on the improved classic method of representatives is presented. First of all, the clustering result is corresponding to the Kripke structure. The relevant system of axioms is chosen by the correspondence between the syntax and semantics of modal logic. Furthermore, a minimal data set which can describe all clustering information is constructed by the implication during formulas. Finally, the validity index is calculated based on the above set. In addition to the validity of a clustering result, this method can also show the structure information of each cluster. Experiments show that this new index has more universal than the current clustering vatiditv indexes.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2009年第8期1997-2002,共6页
Systems Engineering and Electronics
基金
国家高技术研究发展计划(863计划)(2006AA12A106)资助课题
关键词
数据挖掘
聚类评价
代表点
模态逻辑
data mining
clustering validity
representatives
modal logic
