visClust: A visual clustering algorithm based on orthogonal projections

Anna Breger; Clemens Karner; Martin Ehler

doi:10.1016/j.patcog.2023.110136

Titel

visClust: A visual clustering algorithm based on orthogonal projections

Autor*in

Anna Breger

Fakultät für Mathematik, Universität Wien

Clemens Karner

Fakultät für Mathematik, Universität Wien

Martin Ehler

Fakultät für Mathematik, Universität Wien

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 as well 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.

Stichwort

Artificial IntelligenceComputer Vision and Pattern RecognitionSignal ProcessingSoftware

Objekt-Typ

journal article

Sprache

Englisch [eng]

Persistent identifier

phaidra.univie.ac.at/o:2044999

DOI

10.1016/j.patcog.2023.110136

Erschienen in

Titel