Recent convergence results for the fuzzy c-means clustering algorithms

1988 ◽  
Vol 5 (2) ◽  
pp. 237-247 ◽  
Author(s):  
Richard J. Hathaway ◽  
James C. Bezdek
Keyword(s):  
Clustering Algorithms ◽  
Fuzzy C Means ◽  
Convergence Results ◽  
Fuzzy C Means Clustering

Efficient Implementation of the Fuzzy c-Means Clustering Algorithms

1986 ◽  
Vol PAMI-8 (2) ◽  
pp. 248-255 ◽  
Author(s):  
Robert L. Cannon ◽  
Jitendra V. Dave ◽  
James C. Bezdek
Keyword(s):  
Clustering Algorithms ◽  
Efficient Implementation ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

scholarly journals A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Chunhua Ren ◽  
Linfu Sun
Keyword(s):  
Clustering Algorithms ◽  
Real Data ◽  
Local Information ◽  
Data Sets ◽  
Clustering Ensemble ◽  
K Nearest Neighbors ◽  
Fuzzy C Means ◽  
Clustering Quality ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering

AbstractThe classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.

Effective fuzzy c-means clustering algorithms for data clustering problems

2012 ◽  
Vol 39 (7) ◽  
pp. 6292-6300 ◽  
Author(s):  
S.R. Kannan ◽  
S. Ramathilagam ◽  
P.C. Chung
Keyword(s):  
Data Clustering ◽  
Clustering Algorithms ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Clustering Problems

Implementation and Comparative Analysis of k-means and Fuzzy c-means Clustering Algorithms for Tasks Allocation in Distributed Real Time System

2019 ◽  
Vol 10 (2) ◽  
pp. 66-86 ◽  
Author(s):  
Harendra Kumar ◽  
Isha Tyagi
Keyword(s):  
Real Time ◽  
High Performance ◽  
Distributed Processing ◽  
Clustering Algorithms ◽  
Processing System ◽  
Real Time Systems ◽  
Real Time System ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Time Systems

Distributing tasks to processors in distributed real time systems is an important step for obtaining high performance. Scheduling algorithms play a vital role in achieving better performance and high throughput in heterogeneous distributed real time systems. To make the best use of the computational power available, it is essential to assign the tasks to the processor whose characteristics are most appropriate for the execution of the tasks in a distributed processing system. This study develops two algorithms for clustering the heavily-communicating tasks to reduce the inter-tasks communication costs by using k-means and fuzzy c-means clustering techniques respectively. In order to minimize the system cost and response time, an algorithm is developed for the proper allocation of formed clusters to the most suitable processor. The present algorithms are collated with problems in literature. The proposed algorithms are formulated and applied to numerous numerical examples to demonstrate their effectiveness.

Regularized Fuzzy c-Means Clustering and its Behavior at Point of Infinity

2019 ◽  
Vol 23 (3) ◽  
pp. 485-492 ◽  
Author(s):  
Yuchi Kanzawa ◽  
Sadaaki Miyamoto ◽  
◽  
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Fuzzy Classification ◽  
Fuzzy C Means ◽  
Derivative Function ◽  
Fuzzy C Means Clustering ◽  
Classification Function

This study shows that a general regularized fuzzy c-means (rFCM) clustering algorithm, including some conventional clustering algorithms, can be constructed if a given regularizer function value, its derivative function value, and its inverse derivative function value can be calculated. Furthermore, the results of the study show that the behavior of the fuzzy classification function for rFCM at an infinity point is similar to that for some conventional clustering algorithms.

Robust RML Estimator - Fuzzy C-Means Clustering Algorithms for Noisy Image Segmentation

2011 ◽  
pp. 474-486 ◽  
Author(s):  
Dante Mújica-Vargas ◽  
Francisco Javier Gallegos-Funes ◽  
Alberto J. Rosales-Silva ◽  
Rene Cruz-Santiago
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithms ◽  
Noisy Image ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

The cascaded moving k-means and fuzzy c-means clustering algorithms for unsupervised segmentation of malaria images

2015 ◽  
Author(s):  
Aimi Salihah Abdul-Nasir ◽  
Mohd Yusoff Mashor ◽  
Nurul Hazwani Abd Halim ◽  
Zeehaida Mohamed
Keyword(s):  
Clustering Algorithms ◽  
Unsupervised Segmentation ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering
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