一种基于初始点密度最大的改进型ISODATA聚类算法  

An Improved ISODATA Clustering Algorithm Based on the Maximum Initial Density

作　　者：李润青 谢明鸿[1] 黄冰晶 

机构地区：[1]昆明理工大学信息工程与自动化学院,云南昆明650500

出　　处：《软件导刊》2017年第12期94-98,共5页Software Guide

基　　金：云南省教育厅重点项目(KKJD 201403003);云南省级项目(人培)(KKSY201403020;KKSY201403116)

摘　　要：针对ISODATA对初始聚类点选取较为敏感,不能处理噪声点的缺陷,提出一种基于结合密度最大的改进型ISODATA的划分聚类方法 D-ISODATA。基于高局部密度点距离和局部密度最大原则,优化聚类初始点并去除噪声点。根据考察对象所处空间区域的密度分布情况划分基本簇,结合ISODATA聚类算法良好的自适应性,有效地对数据集进行分类。实验表明,这种基于密度聚类的改进型ISODATA算法能有效去除噪声点,改善初始中心点选择对最后聚类算法的影响,并且具有良好的自适应性,对于数据集处理的准确性优于传统K-means算法和ISODATA算法。Aiming at the defect that ISODATA is sensitive to the initial clustering points and can not deal with the noise points,this paper proposes an improved ISODATA clustering method based on the combination of maximum density D-ISODATA.Based on the principle of＂high local density point distance＂and local density maximum principle,the initial points and the noise points are optimized.Through the investigation of the basic object to divide the cluster density distribution area,combined with the ISODATA clustering algorithm is a good＂adaptive＂,classify the data set,experiments show that the improved ISODATA algorithm can effectively remove the noise density clustering based on improved effect on the final selection of the initial center point clustering algorithm,and have good adaptability.The accuracy of data processing is better than the traditional partition based clustering algorithms such as K-means algorithm and IOSDATA algorithm and the clustering algorithm based on density division such as DBSCAN algorithm.

关 键 词：高局部密度点距离 初始点选择 噪声点 ISODATA D-ISODATA算法 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]