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ANALISIS PERBANDINGAN METODE FUZZY C-MEANS DAN SUBTRACTIVE FUZZY C-MEANS [PDF]

Media Statistika, 2015
Fuzzy C-Means (FCM) is one of the most frequently used clustering method. However FCM has some disadvantages such as number of clusters to be prespecified and partition matrix to be randomly initiated which makes clustering result becomes inconsistent ...
Baiq Nurul Haqiqi, Robert Kurniawan
doaj   +6 more sources

Revisiting Possibilistic Fuzzy C-Means Clustering Using the Majorization-Minimization Method [PDF]

Entropy
Possibilistic fuzzy c-means (PFCM) clustering is a kind of hybrid clustering method based on fuzzy c-means (FCM) and possibilistic c-means (PCM), which not only has the stability of FCM but also partly inherits the robustness of PCM.
Yuxue Chen, Shuisheng Zhou
doaj   +2 more sources

SEGMENTASI CITRA MENGGUNAKAN ALGORITMA FUZZY c-MEANS (FCM) DAN SPATIAL FUZZY c-MEANS (sFCM)

Kubik, 2017
Pengolahan citra merupakan salah satu aplikasi yang dimanfaatkan dalam kehidupan. Salah satu kajian pengolahan citra adalah segmentasi. Segmentasi citra dilakukan dengan banyak pendekatan, diantaranya pedekatan klastering.
Qonita Ummi Safitri, Arief Fatchul Huda, Asep Solih Awaludin   +2 more
doaj   +2 more sources

Automatic Genetic Fuzzy c-Means

Journal of Intelligent Systems, 2018
Fuzzy c-means is an efficient algorithm that is amply used for data clustering. Nonetheless, when using this algorithm, the designer faces two crucial choices: choosing the optimal number of clusters and initializing the cluster centers.
Jebari Khalid, Elmoujahid Abdelaziz, Ettouhami Aziz   +2 more
doaj   +2 more sources

Clustering of COVID-19 data for knowledge discovery using c-means and fuzzy c-means [PDF]

Results in Physics, 2021
In this work, the partitioning clustering of COVID-19 data using c-Means (cM) and Fuzy c-Means (Fc-M) algorithms is carried out. Based on the data available from January 2020 with respect to location, i.e., longitude and latitude of the globe, the ...
Asif Afzal, Zahid Ansari, Saad Alshahrani, Arun K. Raj, Mohamed Saheer Kuruniyan, C. Ahamed Saleel, Kottakkaran Sooppy Nisar   +6 more
doaj   +2 more sources

Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms [PDF]

Advances in Fuzzy Systems, 2015
Intuitionistic fuzzy sets (IFSs) provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM)
Arindam Chaudhuri
doaj   +2 more sources

Weighted-covariance factor fuzzy C-means clustering [PDF]

2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2015
In this paper, we propose a factor weighted fuzzy c-means clustering algorithm. Based on the inverse of a covariance factor, which assesses the collinearity between the centers and samples, this factor takes also into account the compactness of the ...
Bertrand, Isabelle, Chabbert, Brigitte, Perrin, Eric, Rammal, Abbas, Vrabie, Valériu   +4 more
core   +6 more sources

Bilateral Weighted Fuzzy C-Means Clustering

Iranian Journal of Electrical and Electronic Engineering, 2012
Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise.
A. H. Hadjahmadi, M. M. Homayounpour, M. Ahadi   +2 more
doaj   +1 more source

K-means and fuzzy c-means algorithm comparison on regency/city grouping in Central Java Province

Desimal, 2022
The Human Development Index (HDI) is very important in measuring the country's success as an effort to build the quality of life of people in a region, including Indonesia. The government needs to make groupings based on the needs of a city/district.
Ummu Wachidatul Latifah, Sugiyarto - Surono, Suparman suparman   +2 more
doaj   +1 more source

Fuzzy c-means with variable compactness [PDF]

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008
Fuzzy c-means (FCM) clustering has been extensively studied and widely applied in the tissue classification of biomedical images. Previous enhancements to FCM have accounted for intensity shading, membership smoothness, and variable cluster sizes. In this paper, we introduce a new parameter called "compactness" which captures additional information of ...
Snehashis, Roy, Harsh, Agarwal, Aaron, Carass, Ying, Bai, Dzung L, Pham, Jerry L, Prince   +5 more
openaire   +2 more sources