# Towards High-Performance Exploratory Data Analysis (EDA) Via Stable Equilibrium Point

arXiv: https://arxiv.org/abs/2306.04425 · HTML: https://mlsanity.com/paper/2306.04425/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 7 Jun 2023.

## Builds on (most cited first, 1 of 1)

- [Accelerate Support Vector Clustering via Spectrum-Preserving Data Compression](https://mlsanity.com/paper/2304.09868/) (2023)

## Abstract (arXiv)

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.