关键词: 高维数据, 可视分析, 子空间探索, 子空间聚类

Abstract: With the rapid development of information technology and the advent of big data era, the data show the complex features of high dimensionality and nonlinearity. For high-dimensional data, it is often difficult to find feature regions that reflect distribution patterns in full-dimensional space, but most of the traditional clustering algorithms only have good scalability for low-dimensional data. Therefore, when the traditional clustering algorithm processes high-dimensional data, the clustering results may not meet the needs of the current stage. The subspace clustering algorithm searches for clusters existing in the high-dimensional data subspace, and divides the original feature space of data into different subsets of features to reduce the influence of uncorrelated features and preserve the main features in the original data. The subspace clustering method can find the information that is not easy to show in high-dimensional data and display the internal structure of data attributes and dimensions through visualization techniques, which provides an effective method for visual analysis of high-dimensional data. This paper summarizes the research progress of high-dimensional data visual analysis methods based on subspace clustering in recent years, and elaborates three different methods based on feature selection, subspace exploration and subspace clustering. Then, the methods and applications of its interaction analysis are analyzed, and the future development trends of visual analysis methods of high-dimensional data are prospected.

Key words: high dimensional data, visual analysis, subspace exploration, subspace clustering