Cluster analysis divides data into clusters that are meaningful, useful, or both. If meaningful clusters are the goal, then the clusters should capture the natural structure of the data. In case of soft clustering techniques, fuzzy sets are used to cluster data, so that each point may belong to two or more clusters with different membership degrees.

This tool proposes the use of two clustering algorithms. The usual Fuzzy C-Means Algorithm and ckMeans Algorithm. The latter is a hybridization between the K-Means Algorithm and Fuzzy C-Means Algorithm, called Fuzzy ckMeansImage, proving to be fast and efficient, as showed in their publications. Also, this algorithm highlights the pixels in which the algorithm did not classify with certain membership degree.

When using this tool, please, cite the papers [1] and [2].

See more about the algorithm at:
[1] de VARGAS, R.; BEDREGAL, B.: A Comparative Study Between fuzzy c-means and ckMeans Algorithms. In: Proc. Conf. North American Fuzzy Information Processing Society (NAFIPS 2010), Toronto, Canada (2010). [Link]

[2] de Vargas R.R., Freddo R., Galafassi C., Gass S.L.B., Russini A., Bedregal B. (2018) Identifying Pixels Classified Uncertainties ckMeansImage Algorithm. In: Medina J., Ojeda-Aciego M., Verdegay J., Perfilieva I., Bouchon-Meunier B., Yager R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2018. Communications in Computer and Information Science, vol 855. Springer, Cham. [Link]

[3] FREDDO, R.; de VARGAS, R.; GALAFASSI, C.; RUSSINI, A.; PINTO, T. H. D.. Desenvolvimento de uma Ferramenta Web para a Execução do Algoritmo Fuzzy ckMeans no Processamento de Imagens. Revista Junior de Iniciação Científica em Ciências Exatas e Engenharia, v. 1, p. 20-27, 2017. [Link]

[4] de VARGAS, R.; FREDDO, R. ; RUSSINI, A. ; AMORIM, N. C. ; GALAFASSI, C. . Identi􏰀cação de áreas agrícolas para manejo diferenciado utilizando o algoritmo ckMeansImage. In: 47 Jornadas Argentinas de Informática (JAIIO), 2018, Buenos Aires. Congreso Argentino de AgroInformática, 2018. v. 1. p. 3-11. [Link]

[5] GASS, S. L. B.; GALAFASSI, C.; de VARGAS, R.. Comparativo entre os algoritmos K-Means e ckMeans para mapeamento automatizado de uso do solo. In: Proc. Conf. Brazilian Symposium on Remote Sensing (SBSR 2017), Santos, Brazil (2017). [Link]

[6] de VARGAS, R.; GALAFASSI, C.; AMORIM, N. C.; FREDDO, R.. Algoritmo ckMeans Aplicado ao Sensoriamento Remoto. In: Proc. Conf. Congresso Nacional de Matemática Aplicada e Computacional (CNMAC 2016), Gramado, Brazil (2016). [Link]

[7] de VARGAS, R.; BEDREGAL, B.; DIMURO, G. P.. Using ckMeans algorithm in image segmentation process: Preliminary results on mammography analysis. In: Proc. Conf. Congresso de Matemática Aplicada e Computacional (CNMAC 2014), Natal, Brazil (2014). [Link]

[8] de VARGAS, R.: Uma Nova Forma de Calcular os Centros dos Clusters em Algoritmos de Agrupamento Tipo Fuzzy C-Means. PhD thesis, Universidade Federal do Rio Grande do Norte, Natal, Brasil (2012). [Link]


Process Image Status Details Remove



Fuzzy ckMeansImage
Beta Version

Server Details

Description Value
Processor Intel(R) Xeon(R) CPU E5430
Clock 2.66 GHz
Memory RAM 8.16 Gb
Cache Size 6144 KB
Operation System Ubuntu 18.04.1 LTS


Developers Ricardo Freddo and Rogério R. de Vargas
Local Federal University of Pampa
Powered by:

Create Account

Edit Account

Recover your account


Fuzzy ckMeansImage

Fuzzy C-Means