摘要
针对单一聚类方法远不能满足实际数据分析需求,且K-Means聚类中维数高,非度量型数据分析亟待解决的问题,提出一种基于非度量多维缩放的聚类组合算法(NMDSCCA)。该算法通过非度量多维缩放方法对非度量型的高维数据进行降维,利用降维后得到的主成分变量作为输入变量,以K-Means算法作为基聚类器进行聚类,解决了K-Means算法无法处理分类数据以及维数高的变量局限性,使其具有普适性。仿真实验表明,新算法不仅聚类效果上均优于传统K-Means算法及基于主成分分析(PCA)的聚类组合算法,而且算法应用于大数据时具有更高的收敛速度。
Concerning the problem about real and complex data analysis not being met by single clustering method and non-metric and high-dimensional variables exited in K-Means algorithm, a Clustering Combination Algorithm based on Nonmetric MultiDimensional Sealing （NMDSCCA） was proposed. Firstly, the non-metric multi-dimensional scaling method was used to reduce the dimension. Then, using the principal component variables obtained after dimensionality reduction as input variables, and the K-Means algorithm as a base classifier for clustering, The limitations existed in K-Means algorithm about the classification of data and high-dimensional variable were solved and the algorithm was made universal. The simulation results show that the algorithm not only has advantages over both traditional K-Means algorithm and clustering algorithm based on Principal Component Analysis （PCA） in cluster performance experiments, but also has high convergence speed when dealing with big data.
作者
周文娟
赵礼峰
ZHOU Wenjuan;ZHAO Lifeng(College of Science,Nanjing University of Posts and Telecommunications,Nanjing Jiangsu 210023,China)
出处
《计算机应用》
CSCD
北大核心
2018年第A01期67-72,共6页
journal of Computer Applications
基金
国家自然科学基金青年基金资助项目(61304169)
关键词
非度量多维缩放
K—Means算法
聚类分析
聚类组合
高维数据
主成分分析
non-metric multidimensional scaling
K-Means algorithm
clustering analysis
clustering combination
highdimensional data
Principal Component Analysis （PCA）
