作　　者： ; ; ; ;

机构地区： 中国矿业大学计算机科学与技术学院

出　　处： 《计算机研究与发展》 2007年第4期674-679,共6页

摘　　要： 提出了基于广义信息距离的直接聚类算法.基于信息理论给出了离散量的基本概念,讨论并证明了离散量的一个基本不等式,进而给出了离散增量的概念.在分析距离测度的基础上,提出了广义信息距离(GID)、改进的广义信息距离(IGID),建立了基于GID,IGID的直接聚类算法,并对土地肥力数据资料进行了聚类分析.结果表明,建立的算法与传统的聚类算法相比,算法原理简便、对数据本身的维数与分布要求不高,且具有较好的聚类效果. In this paper a novel direct clustering algorithm based on generalized information distance （GID） is put forward. Firstly, based on information theory, a basic concept of measure of diversity is given and an inequality about measure of diversity is proved. Based on this inequality, a concept of increment of diversity is discussed and a defined. Secondly, by analyzing distance measure, two new concepts of generalized information distance （GID） and improved generalized information distance （IGID） are proposed, and a new direct clustering algorithm based on GID and IGID is designed. Finally this algorithm is applied to soil fertility data processing, and compared with hierarchical clustering algorithm （HCA） . The results of simulation application show that the algorithm presented here is feasible and effective. Because of simplicity of algorithm and robustness. It provides a new research approach for studies of pattern recognition theory.

关 键 词： 离散量 离散增量 广义信息距离 直接聚类

领　　域： [自动化与计算机技术] [自动化与计算机技术]