# Visualization tools for parameter selection in cluster analysis

## TL;DR

This paper introduces HPREF, an algorithm that visualizes the structure of multiple clusterings of a dataset, aiding parameter selection in cluster analysis.

## Contribution

The paper presents HPREF, a novel hierarchical partitioning algorithm that visualizes the space of clusterings generated by varying parameters.

## Key findings

- Provides a geometric visualization of clustering results
- Helps identify optimal parameters for clustering algorithms
- Enhances understanding of clustering stability and variability

## Abstract

We propose an algorithm, HPREF (Hierarchical Partitioning by Repeated Features), that produces a hierarchical partition of a set of clusterings of a fixed dataset, such as sets of clusterings produced by running a clustering algorithm with a range of parameters. This gives geometric structure to such sets of clustering, and can be used to visualize the set of results one obtains by running a clustering algorithm with a range of parameters.