  Exploratory data analysis (EDA) is a vital procedure for data science
projects. In this work, we introduce a stable equilibrium point (SEP) - based
framework for improving the efficiency and solution quality of EDA. By
exploiting the SEPs to be the representative points, our approach aims to
generate high-quality clustering and data visualization for large-scale data
sets. A very unique property of the proposed method is that the SEPs will
directly encode the clustering properties of data sets. Compared with prior
state-of-the-art clustering and data visualization methods, the proposed
methods allow substantially improving computing efficiency and solution quality
for large-scale data analysis tasks.
