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Visual analytics for the clustering capability of data

Science China(Information Sciences) 2013年第5期56卷 131-144页

作者： LU ZhiMao LIU Chen ZHANG Qi ZHANG ChunXiang FAN DongMei YANG Peng Pattern Recognition and Natural Computation Laboratory Harbin Engineering University School of Computer Science and Technology Dalian University of Technology School of Software Harbin University of Science and Technology

Clustering analysis is an unsupervised method to find hidden structures in datasets and has been widely used in various ***,it is always difficult for users to understand,evaluate,and explain the clustering results in the spaces with dimension greater than *** high-dimensional visualization of clustering technology can express clustering results well,it still has significant *** this paper,a visualization cluster analysis method based on the minimum distance spectrum(MinDS) is proposed,aimed at reducing the problems of clustering multidimensional ***,the concept of MinDS is defined based on the distance between high-dimensional *** can map any dataset from high-dimensional space to a lower dimension to determine whether the data set is ***,a clustering method which can automatically determine the number of categories is designed based on *** method is not only able to cluster a dataset with clear boundaries,but can also cluster the dataset with fuzzy boundaries through the edge corrosion strategy based on the energy of each data *** addition,strategies for removing noise and identifying outliers are designed to clean datasets according to the characteristics of *** experimental results presented validate the feasibility and effectiveness of the proposed schemes and show that the proposed approach is simple,stable,and efficient,and can achieve multidimensional visualization cluster analysis of complex datasets.

关键词： data mining clustering analysis visual analysis minimum distance spectrum nearest neighbor spectrum outliers

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