一种基于离群指数的初始聚类中心优选算法  

The Optimal Initial Centers Clustering Algorithm Based on Local Outlier Factor

作　　者：李向[1] 刘素红[2,3] 

机构地区：[1]郑州航空工业管理学院计算机科学与应用系,河南郑州450015 [2]北京师范大学地理学与遥感科学学院,北京100875 [3]北京师范大学遥感科学国家重点实验室,北京100875

出　　处：《微电子学与计算机》2013年第6期109-112,共4页Microelectronics & Computer

基　　金：国家自然科学基金项目(41001235);航空科学基金项目(2011ZC55005)

摘　　要：传统K-均值聚类算法的初始聚类中心是随机选择的,不同的初始聚类中心会得到不同的聚类结果,聚类结果随机性较大、稳定性差.采用局部离群指数优化K-均值聚类算法,通过计算所有数据样本的局部离群指数,选择k个相互距离最远的局部密集点作为初始聚类中心,消除局部离群点对算法的影响.实验结果证明,该算法能降低K-均值聚类算法初始聚类中心选取的敏感度,减少迭代次数,得到更为准确的聚类结果.The initial clustering centers of traditional K-means clustering algorithm are generated randomly. Different initial clustering centers will lead to different clustering results. It means that unstable results were often obtained owing to random selection of initial centers while using traditional K-means. A new method about optimization of initial centers is brought forward based on K-means clustering algorithm which can gain high accurate results. The main idea of the improved algorithm is to choose the K-means initial clustering centers through calculating the local outlier factor of all sample data. The greatest distance dense points are chosen to eliminate the influence of local outliers. The experimental results show that the improved algorithm can reduce the selection sensitivity of the K- means initial clustering centers, and also get more accurate clustering results with less iterations.

关 键 词：聚类 离群指数 初始聚类中心K-均值聚类 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]