<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Suppressed fuzzy c-means clustering was proposed as an attempt to combine the better properties of hard and fuzzy c-means clustering, namely the quicker convergence of the former and the finer partition quality of the latter. In the meantime, it became much more than that. Its competitive behavior was revealed, based on which it received two generalization schemes. It was found a close relative of the so-called fuzzy c-means algorithm with generalized improved partition, which could improve its popularity due to the existence of an objective function it optimizes. Using certain suppression rules, it was found more accurate and efficient than the conventional fuzzy c-means in several, mostly image processing applications. This paper reviews the most relevant extensions and generalizations added to the theory of fuzzy c-means clustering models with suppressed partitions, and summarizes the practical advances these algorithms can offer.</p></abstract>

Suppressed fuzzy c-means clustering was proposed as an attempt to combine the better properties of hard and fuzzy c-means clustering, namely the quicker convergence of the former and the finer partition quality of the latter. In the meantime, it became much more than that. Its competitive behavior was revealed, based on which it received two generalization schemes. It was found a close relative of the so-called fuzzy c-means algorithm with generalized improved partition, which could improve its popularity due to the existence of an objective function it optimizes. Using certain suppression rules, it was found more accurate and efficient than the conventional fuzzy c-means in several, mostly image processing applications. This paper reviews the most relevant extensions and generalizations added to the theory of fuzzy c-means clustering models with suppressed partitions, and summarizes the practical advances these algorithms can offer.

A review on suppressed fuzzy c-means clustering models

Sapientia Hungarian University of Transylvania

University Research, Innovation and Service Center

Acta Universitatis Sapientiae, Informatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"A review on suppressed fuzzy c-means clustering models"}

Suppressed fuzzy c-means clustering was proposed as an attempt to combine the better properties of hard and fuzzy c-means clustering, namely the quicker...

A review on suppressed fuzzy c-means clustering models

Online veröffentlicht: 29. Jan. 2021

Seitenbereich: 302 - 324

Eingereicht: 15. Nov. 2020

Akzeptiert: 21. Nov. 2020

DOI: https://doi.org/10.2478/ausi-2020-0018

Schlüsselwörter
fuzzy c-means algorithm, suppressed fuzzy c-means algorithm, image segmentation, data mining

© 2020 László Szilágyi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A review on suppressed fuzzy c-means clustering models

Online veröffentlicht: 29. Jan. 2021

Seitenbereich: 302 - 324

Eingereicht: 15. Nov. 2020

Akzeptiert: 21. Nov. 2020

DOI: https://doi.org/10.2478/ausi-2020-0018

Schlüsselwörterfuzzy c-means algorithm, suppressed fuzzy c-means algorithm, image segmentation, data mining

© 2020 László Szilágyi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
fuzzy c-means algorithm, suppressed fuzzy c-means algorithm, image segmentation, data mining