计算机应用研究2016，Vol.33Issue(8)：2331-2334,4.DOI:10.3969/j.issn.1001-3695.2016.08.021
基于图谱理论几何空间结构变换的大数据核聚类算法
Spectral graph geometric transform based kernel clustering approach for big scale data with high computer efficiency
摘要
Abstract
For the problem that the existing kernel clustering approaches need to learn the entire kernel matrix with the low compute efficiency,aimed at it proposed a clustering approach based on the spectral.Firstly,it based on spectral construct the similarity graph;then,computed the Laplacian matrix for the graph and select little part of the matrix to learn;lastly,realized the classification with kernel K-means approach.The simulating results prove that the proposed approach has better compute efficiency than the other kernel clustering approaches with a comparable clustering performance and works for big scale data.关键词
核函数/核聚类/几何空间变换/核矩阵/大规模数据/拉普拉斯矩阵/最近邻相似度Key words
kernel function/kernel clustering/geometric transform/kernel matrix/big scale data/Laplacian matrix/nearest neighbor分类
信息技术与安全科学引用本文复制引用
邹汪平,方元康,吴伟..基于图谱理论几何空间结构变换的大数据核聚类算法[J].计算机应用研究,2016,33(8):2331-2334,4.基金项目
国家自然科学基金资助项目（61100034，61170043）；中国博士后科学基金资助项目（20110491411）；江苏省博士后科研计划资助项目（1101092C）；安徽省高校省级科学研究项目（KJ2011B108）；安徽省高等学校省级质量工程项目（2015gxk113，2014jyxm524，2013jxtd065）；安徽省2016年高校优秀青年人才支持计划重点项目 （）
