visClust: A visual clustering algorithm based on orthogonal projections
This is an incremental improvement for researchers and practitioners in data clustering, offering a method with one obligatory input parameter and competitive results.
The authors tackled the problem of clustering by introducing visClust, a novel algorithm based on orthogonal projections and visual interpretation, which achieved superior performance in most experiments compared to 6 state-of-the-art algorithms, as measured by accuracy and adjusted Rand-Index, while requiring low runtime and RAM.
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.