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Exploratory data analysis (EDA) is a vital procedure for data scienceprojects. In this work, we introduce a stable equilibrium point (SEP) - basedframework for improving the efficiency and solution quality of EDA. Byexploiting the SEPs to be the representative points, our approach aims togenerate high-quality clustering and data visualization for large-scale datasets. A very unique property of the proposed method is that the SEPs willdirectly encode the clustering properties of data sets. Compared with priorstate-of-the-art clustering and data visualization methods, the proposedmethods allow substantially improving computing efficiency and solution qualityfor large-scale data analysis tasks.

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