Visual Clustering Method of Quasi-Circular Mapping Based on Dimension Extension and Rearrangement
HUANG Shan1, LI Ming1,2, CHEN Hao1,2, LI Junhua1,2, ZHANG Congxuan2
1.School of Information Engineering, Nanchang Hangkong University, Nanchang 330063 2.Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang 330063
Abstract 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
Fund:Supported by National Natural Science Foundation of China(No.61772255,61866025,61866026), Natural Science Foundation of Jiangxi Province(No.20181BAB202025), Jiangxi Superiority Science and Technology Innovation Team Project(No.20181BCB 24008), Jiangxi Province Innovation Drives “5511” Project Advantage Discipline Innovation Team(No.20165BCB19007), Science and Technology Project of Jiangxi Education Department(No.GJJ170608), Jiangxi Postgraduate Innovation Project(No.YC2017-S327)
About author:: HUANG Shan, master student. Her research interests include multi-objective visualization and visual clustering of high dimensional data.LI Ming, Ph.D., professor. His research interests include image processing, pattern recognition and multi-objective optimization problem.CHEN Hao(Corresponding author), Ph.D., associate professor. His research interests include evolutionary algorithms, image processing and pattern recognition.
HUANG Shan,LI Ming,CHEN Hao等. Visual Clustering Method of Quasi-Circular Mapping Based on Dimension Extension and Rearrangement[J]. , 2019, 32(4): 326-335.
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2019, Vol. 32 
