新的基于最近邻聚类的属性离散化算法  

Novel attribute discretization algorithm based on nearest neighbor-clustering

作　　者：王杰[1] 姜国强[1] 

机构地区：[1]郑州大学电气工程学院,郑州450001

出　　处：《计算机工程与应用》2009年第24期40-42,共3页Computer Engineering and Applications

基　　金：河南省杰出人才创新基金No074200510013;河南省教育厅自然科学基金No2007520048~~

摘　　要：连续属性离散化是知识发现研究中重要的预处理过程,基于最近邻聚类和粗集的相关理论,提出一种新的有监督的多属性离散化方法。该算法分两个阶段来处理,首先利用最近邻聚类动态调整聚类的类别数,生成初始聚类。然后基于类信息的相似性定义合并相似区间,减少了聚类区间。通过实例分析,该算法是非常有效的。Discretization of continuous attribute is an important pretreatment process in the knowledge discovery study.A novel algorithm of supervised discretization of continuous attributes based on nearest neighbor-clustering and the theories of rough set is introduced.The algorithm is dealt with in two stages,in the first stage,the number of clusters is adjusted dynamically by using the nearest neighbour-clustering,and then the initial clusters are determined.In the second stage,the adjacent region is merged by ad...

关 键 词：离散化 最近邻聚类 粗集 近似分类质量 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]