visClust: A visual clustering algorithm based on orthogonal projections

TL;DR

visClust is a new visual clustering algorithm that transforms data into a binary image format, enabling effective partitioning with minimal parameters and demonstrating superior performance over existing methods.

Contribution

The paper introduces visClust, a clustering method leveraging orthogonal projections and image processing, requiring only one main parameter and showing improved accuracy and efficiency.

Findings

Performs well in accuracy and adjusted Rand-Index

Requires low runtime and RAM

Outperforms 6 state-of-the-art algorithms in most tests

Abstract

We present a novel clustering algorithm, visClust, that is based on lower dimensional data representations and visual interpretation. Thereto, we design a transformation that allows the data to be represented by a binary integer array enabling the use of image processing methods to select a partition. Qualitative and quantitative analyses measured in accuracy and an adjusted Rand-Index show that the algorithm performs well while requiring low runtime and RAM. We compare the results to 6 state-of-the-art algorithms with available code, confirming the quality of visClust by superior performance in most experiments. Moreover, the algorithm asks for just one obligatory input parameter while allowing optimization via optional parameters. The code is made available on GitHub and straightforward to use.