# By chance is not enough: Preserving relative density through non uniform sampling

## TL;DR

This paper introduces a non-uniform sampling method for 2D scatter-plots that effectively preserves relative densities, addressing visualization challenges with large datasets by modeling image features and using metrics to guide sampling.

## Contribution

It presents a novel formal environment and metrics for modeling image features, enabling automatic non-uniform sampling that maintains relative densities in large data visualizations.

## Key findings

- Effective preservation of relative densities in scatter-plots
- Improved visibility of data features in large datasets
- Automatic sampling technique outperforms uniform methods

## Abstract

Dealing with visualizations containing large data set is a challenging issue and, in the field of Information Visualization, almost every visual technique reveals its drawback when visualizing large number of items. To deal with this problem we introduce a formal environment, modeling in a virtual space the image features we are interested in (e.g, absolute and relative density, clusters, etc.) and we define some metrics able to characterize the image decay. Such metrics drive our automatic techniques (i.e., not uniform sampling) rescuing the image features and making them visible to the user. In this paper we focus on 2D scatter-plots, devising a novel non uniform data sampling strategy able to preserve in an effective way relative densities.