visClust: A visual clustering algorithm based on orthogonal projections

Anna Breger (Korresp. Autor*in), Clemens Karner (Korresp. Autor*in), Martin Ehler (Korresp. Autor*in)

Veröffentlichungen: Beitrag in FachzeitschriftArtikelPeer Reviewed

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.

OriginalspracheEnglisch
Aufsatznummer110136
FachzeitschriftPattern Recognition
Jahrgang148
DOIs
PublikationsstatusVeröffentlicht - Apr. 2024

ÖFOS 2012

  • 102035 Data Science
  • 102019 Machine Learning

Fingerprint

Untersuchen Sie die Forschungsthemen von „visClust: A visual clustering algorithm based on orthogonal projections“. Zusammen bilden sie einen einzigartigen Fingerprint.

Zitationsweisen