Network-based clustering and embedding for high-dimensional data visualization

We present a novel method to visualize high-dimensional dataset as a landscape. The goal is to provide clear and compact representation to reveal the structure of high-dimensional datasets in a way that the size and distinctiveness of clusters can be easily discerned, and the relationships among single points can be preserved. Our method is network-based, and consists of two main steps: clustering and embedding. First of all, the similarity graph of high-dimensional dataset is constructed based on the Euclidean distances between data points. For clustering, we propose a new network community detection algorithm to calculate the membership-degree of each vertex belonging to each community. For embedding, we bring forward a practical algorithm to obtain an evenly distributed and regularly shaped layout of data points, in a way that the original relationships among single points are preserved. Finally, the landscape-like visualization is produced by assigning altitudes to data points according to their membership-degrees and by inserting control points. In our high-dimensional data visualization, clusters form highlands, and border data points among clusters show up as valleys. The area and altitude of highland indicate the size and distinctiveness of data cluster respectively.