A review on suppressed fuzzy c-means clustering models
| Autor: | David Iclanzan, Laszlo Szilagyi, László Lefkovits |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Computer science business.industry Geography Planning and Development Pattern recognition suppressed fuzzy c-means algorithm data mining QA75.5-76.95 02 engineering and technology 01 natural sciences Fuzzy logic fuzzy c-means algorithm Electronic computers. Computer science 010608 biotechnology 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Cluster analysis image segmentation 62h30 |
| Zdroj: | Acta Universitatis Sapientiae: Informatica, Vol 12, Iss 2, Pp 302-324 (2020) |
| ISSN: | 2066-7760 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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