计算机应用与软件Issue(4)：178-181,192,5.DOI:10.3969/j.issn.1000-386x.2014.04.045
基于近邻图的k-means初始中心选择调优算法
NEIGHBOURHOOD GRAPH-BASED K-MEANS INITIAL CENTRE SELECTION AND TUNING ALGORITHM
胡湘萍1
作者信息
- 1. 解放军信息工程大学 河南 郑州 450002; 河南经贸职业学院 河南 郑州 450018
- 折叠
摘要
Abstract
K-means clustering algorithm is widely used in the fields of data mining,machine learning and computer vision for its conceptually simplicity and high computation efficiency.However,its performance severely relies on the initial clustering centre selection.Differentinitial cluste-ring centre results in the clustering results of k-means algorithm sharply varying.A reasonable solution is to choose the data sample in the region with relative dense data as the initial clustering centre.In view of this,we propose a data neighbourhood graph-basedinitial centre selection method for k-means algorithm,which takes three steps.The first step is to construct the neighbourhood graph of the dataset.The second step is to choose candidates collection of initial clustering centres.The last step is to decide appropriate initialclustering centre.Experimental results show that the initial clustering centre chosen by the proposed method is reasonable,and can speed up the convergence of k-means at the same time.关键词
聚类/K 均值/初始化/近邻图Key words
Clustering/k-means/Initialisation/Neighbourhood graph分类
信息技术与安全科学引用本文复制引用
胡湘萍..基于近邻图的k-means初始中心选择调优算法[J].计算机应用与软件,2014,(4):178-181,192,5.
