摘要
现有的径向布局可视化方法无法有效捕获高维数据的非线性结构。因此,文中提出基于维度扩展和重排的类圆映射可视化聚类方法。利用近邻传播聚类算法和多目标聚类可视化评价指标对高维数据进行维度扩展,然后对扩展后的高维数据进行维度相关性重排,最后利用类圆映射机制降维至二维可视化空间,实现高维数据有效可视化聚类。实验表明,文中提出的维度扩展和重排策略能有效提高类圆映射可视化方法聚类效果,其中的维度扩展策略也能显著提高其它径向布局可视化方法聚类效果,泛化性能较好。此外,相比同类方法,文中方法在可视化聚类准确度、拓扑保持、Dunn指数及效果上优势明显。
The non-linear structure of high-dimensional data cannot be captured by the existing radial layout visualization method.Therefore,visual clustering method of quasi-circular mapping based on dimension extension and rearrangement is proposed.The dimension of high-dimensional data is expanded by affinity propagation clustering algorithm and multi-objective clustering visualization evaluation index.Then,the dimension correlation rearrangement of the extended high-dimensional data is carried out.Finally,the high-dimensional data is reduced to two-dimensional visualization space by quasi-circular mapping mechanism to realize effective visual clustering.Experiments show that the proposed dimension extension and rearrangement strategy can effectively improve the visual clustering effect of quasi-circular mapping visualization.The dimension extension strategy can also significantly improve the clustering effect of other radial layout visualization methods with better generalization performance.Moreover,the proposed method has obvious advantages in visual clustering accuracy,topology preservation,Dunn index and effect compared with similar methods.
作者
黄珊
黎明
陈昊
李军华
张聪炫
HUANG Shan;LI Ming;CHEN Hao;LI Junhua;ZHANG Congxuan(School of Information Engineering,Nanchang Hangkong University,Nanchang 330063;Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition,Nanchang Hangkong University,Nanchang 330063)
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2019年第4期326-335,共10页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61772255,61866025,61866026)
江西省自然科学基金项目(No.20181BAB202025)
江西省优势科技创新团队计划项目(No.20181BCB24008)
江西省创新驱动“5511”工程优势学科创新团队(No.20165BCB19007)
江西省教育厅科学技术项目(No.GJJ170608)
江西省研究生创新专项资金项目(No.YC2017-S327)资助~~
关键词
类圆映射可视化
维度扩展
可视化聚类
高维数据
Quasi-Circular Mapping Visualization
Dimension Extension
Visual Clustering
High-Dimensional Data
