Abstract: Visual clustering analysis makes use of visualization and interaction technologies to help
users analyze the clustering process and results from multiple perspectives to find hidden structures
and relationships within the original data. However, because of the “curse of dimension” of
high-dimensional data, there are many challenges posed for cluster analysis, such as parameter setting
of clustering model, data feature capture, result interpretation and visualization. Starting with the
problems encountered in the process of high-dimensional data clustering, this paper firstly
summarizes the data processing methods commonly used in the process of clustering and compares
their performance. These methods can greatly solve the “curse of dimension” problem to help users
explore the clustering patterns existing in the data. Then, due to the different needs of the clustering
results obtained by different data processing methods in analyzing and understanding the internal
structure and rules hidden in clusters, this paper makes a summary and divides the currently available
visual analysis approaches of clustering high-dimensional data into two categories, namely, visual
analysis approaches based on dimensionality reduction and subspace clustering. Finally, the current opportunities and challenges existing in this field are discussed.

Key words: visual analysis, clustering, high-dimensional data, overviewing