一种基于自适应膨胀因子的聚类新方法  

New Approach of Clustering Based on Adaptive Inflation Factor

作　　者：牟廉明 黄国兴[2] 

机构地区：[1]内江师范学院计算机系,内江641112 [2]华东师范大学计算机系,上海200062

出　　处：《计算机工程》2003年第9期100-102,共3页Computer Engineering

摘　　要：针对传统聚类方法存在的不足，提出了一种基于自适应膨胀因子的聚类新方法（CAIF）。相对于现有的一些聚类方法，CAIF方法不需要用户确定类数k值，克服了聚类结果对k值的敏感性；与数据的输入顺序无关，能够进行增量聚类。同时，CAIF方法还能有效地发现孤立点。最后通过实验验证了该方法的有效性。In the face of the deficiencies in the traditional clustering methods, this paper puts forward a new clustering based on adaptive inflation factor(CAIF) approach which is different from some existent clustering methods. This approach does not depend on the number of class sorts that has big influence on clustering result. CAIF, which is independent of data input order, can be used in incremental clustering. At the same time, CAIF can find isolated points efficiently. The experiments sufficiently prove the validity of this approach.

关 键 词：自适应 膨胀因子 增量聚类 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]