A new dimensionality reduction-based visualization approach for massive data

Abstract

We live in a big data and data analytics era. The volume, velocity, and variety of data generated today require special methods and techniques for data analysis and inferencing. Data visualization tools allow us to understand the data deeper. One of the straightforward ways of multidimensional data visualization is based on dimensionality reduction and illustrated by a scatter plot. However, visualization of millions of points in a scatter plot does not make a sense. Usually, data sampling or clustering is performed before visualization to reduce the amount of the visualized points, but in such a case, meaningful outliers can be rejected and will not be visualized. In this paper, a new approach for massive data visualization without point overlapping is proposed and investigated. The approach consists of two main stages: selection of a data subset and its visualization without overlapping. The experiments have been carried out with ten data sets. The efficiency of subset selection and visualization of data subset projection is confirmed by a comprehensive set of comparisons.