摘要
通过对标称数据的分析,提出了一种基于信息熵和层次聚类思想的标称数据聚类算法。算法采用信息熵度量对象之间的相似性,通过数据直接计算相似性阈值。实验证明算法是可行并且有效的。
Through analyzing the characteristics of nominal data, clustering algorithm of nominal data based on entropy and hierarchical method was proposed. In this algorithm, similarity between objects was measured by using entropy and similarity threshold was calculated directly by using data. The experimental results show that this algorithm is feasible and effective.
出处
《计算机应用》
CSCD
北大核心
2006年第8期1904-1905,共2页
journal of Computer Applications
关键词
信息熵
聚类
标称变量
entropy
clustering
nominal data
