A Gravitational Fuzzy C-Means Clustering Algorithm Based on Density Weight

2012 ◽  
Vol 182-183 ◽  
pp. 1681-1685
Author(s):  
Tian Wu Zhang ◽  
Gong Bing Guo
Keyword(s):  
Local Minimum ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Noise Data ◽  
Global Optimal ◽  
Minimum Points ◽  
Better Than

Fuzzy C-means clustering algorithm(FCM) is sensitive to its initialization of value and noise data and easy to fall into local minimum points, while it can’t get the global optimal solution. This paper introduces gravitation and density weight into the process of clustering, and proposes a gravitational Fuzzy C-Means clustering algorithm based on density weight (DWGFCM). The experimental results show that the algorithm has better global optimal solution, overcomes the shortcomings of traditional Fuzzy C-means clustering algorithm. Clustering results are obviously better than FCM algorithm.

Fuzzy Clustering Segmentation Algorithm Research on Sports Image

2013 ◽  
Vol 339 ◽  
pp. 297-300 ◽  
Author(s):  
Xue Jiao Dong ◽  
Xiao Yan Zhang
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Practical Method ◽  
Global Optimal Solution ◽  
Fcm Algorithm ◽  
Fast Convergence Rate ◽  
Fuzzy C Means Clustering ◽  
Global Optimal ◽  
Theory Research

In this paper, linking with the basic principle of FCM (Fuzzy c-means clustering) algorithm, on the basis of theory research, a method of the cluster analysis of FCM is proposed. Firstly, the approximate optimal solution obtained by the improved FCM algorithm is taken as the original value of the FCM algorithm, then carrying on the local search to obtain the global optimal solution, the final segmentation result is achieved at last. The experiment results prove that in the view of the flame image segmentation, this method shows the good clustering performance and fast convergence rate, and has the widespread serviceability, so it is the practical method in image segmentation.

scholarly journals A Novel Fuzzy Entropy-Based Method to Improve the Performance of the Fuzzy C-Means Algorithm

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 554 ◽  
Author(s):  
Barbara Cardone ◽  
Ferdinando Di Martino
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Fuzzy Entropy ◽  
Entropy Function ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Random Initialization ◽  
Fuzzy C Means Algorithm ◽  
Optimal Cluster

One of the main drawbacks of the well-known Fuzzy C-means clustering algorithm (FCM) is the random initialization of the centers of the clusters as it can significantly affect the performance of the algorithm, thus not guaranteeing an optimal solution and increasing execution times. In this paper we propose a variation of FCM in which the initial optimal cluster centers are obtained by implementing a weighted FCM algorithm in which the weights are assigned by calculating a Shannon Fuzzy Entropy function. The results of the comparison tests applied on various classification datasets of the UCI Machine Learning Repository show that our algorithm improved in all cases relating to the performances of FCM.

Improved Method of Hybrid Genetic Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 4014-4017
Author(s):  
Lei Ding ◽  
Yong Jun Luo ◽  
Yang Yang Wang ◽  
Zheng Li ◽  
Bing Yin Yao
Keyword(s):  
Genetic Algorithm ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Local Optimum ◽  
Global Optimal Solution ◽  
Global Optimal ◽  
Better Than

On account of low convergence of the traditional genetic algorithm in the late,a hybrid genetic algorithm based on conjugate gradient method and genetic algorithm is proposed.This hybrid algorithm takes advantage of Conjugate Gradient’s certainty, but also the use of genetic algorithms in order to avoid falling into local optimum, so it can quickly converge to the exact global optimal solution. Using Two test functions for testing, shows that performance of this hybrid genetic algorithm is better than single conjugate gradient method and genetic algorithm and have achieved good results.

The Research on Modified K-Means Algorithm Based on GA&SA

2013 ◽  
Vol 347-350 ◽  
pp. 3242-3246
Author(s):  
Zhe Feng Zhu ◽  
Xiao Bin Hui ◽  
Yi Qian Cao ◽  
Wan Xiang Lian
Keyword(s):  
Local Search ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Global Search ◽  
Local Optimization ◽  
Effective Algorithm ◽  
Global Optimal Solution ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Clustering Center

The traditional K-means clustering algorithm has the disadvantage of weakness in overall search, easily falling into local optimization, highly reliance on initial clustering center. Aiming at the drawback of falling into partial optimization, putting forward a modified K-means algorithm mixing GA and SA, which combined the advantages of global search ability of GA and local search, to avoid K-means algorithm to lost into local optimal solution. The results of simulation show that the performance of above-mentioned algorithm is better in the optimization capacity than before, and easier to get the global optimal solution. It is an effective algorithm.

Yarn Selection Based on Simulated Annealing Genetic Algorithm

2014 ◽  
Vol 687-691 ◽  
pp. 1548-1551
Author(s):  
Li Jiang ◽  
Gang Feng Yan ◽  
Zhen Fan
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Original Image ◽  
Separation Algorithm ◽  
Global Optimal Solution ◽  
Simulated Annealing Genetic Algorithm ◽  
Color Separation ◽  
Global Optimal

Aiming at the bad performance when achieve rich colors of fabric with very limited yarns in the traditional woven industry, the paper comes up with a solution of selecting yarn from a set of yarns based on SAGA(simulated annealing genetic algorithm). In order to reduce the computational complexity, original image is compressed based on clustering algorithm. And the original yarns is divided into four regions based on color separation algorithm to narrow the feasible area. The result of experiments show that image compression and yarns division can greatly improve the speed of SAGA, and SAGA can effectively converges to global optimal solution.

Embedding Global Optimization and Kernelization into Fuzzy C-Means Clustering for Consonant/Vowel Segmentation

2013 ◽  
Vol 419 ◽  
pp. 814-819
Author(s):  
Xian Zang ◽  
Kil To Chong
Keyword(s):  
Global Optimization ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Cluster Center ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Simulation Results

This paper proposes a novel clustering algorithm named global kernel fuzzy-c means (GK-FCM) to segment the speech into small non-overlapping blocks for consonant/vowel segmentation. This algorithm is realized by embedding global optimization and kernelization into the classical fuzzy c-means clustering algorithm. It proceeds in an incremental way attempting to optimally add new cluster center at each stage through the kernel-based fuzzy c-means. By solving all the intermediate problems, the final near-optimal solution is determined in a deterministic way. This algorithm overcomes the well-known shortcomings of fuzzy c-means and improves the clustering accuracy. Simulation results demonstrate the effectiveness of the proposed method in consonant/vowel segmentation.

A Smooth Clustering Algorithm Based on Parameter Free Filled Function

2010 ◽  
Vol 143-144 ◽  
pp. 389-393
Author(s):  
Qing Wu ◽  
Li Xing Yuan
Keyword(s):  
Optimization Model ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Filled Function ◽  
Filled Function Method ◽  
Global Optimal Solution ◽  
Entropy Factor ◽  
Global Optimal ◽  
Clustering Center ◽  
Entropy Technique

In this paper, we propose an algorithm to find centers of clusters based on adjustable entropy technique. A completely differentiable non-convex optimization model for the clustering center problem is constructed. A parameter free filled function method is adopted to search for a global optimal solution of the optimization model. The proposed algorithm can avoid the numerical overflow phenomenon. Numerical results illustrate that the proposed algorithm can effectively hunt centers of clusters and especially improve the accuracy of the clustering even with a relatively small entropy factor.

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

scholarly journals Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaqin Zhang ◽  
Jingan Wang ◽  
Le Xing ◽  
Hui’e Liang
Keyword(s):  
Industrial Application ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Measurement ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Environmental Robustness

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

Sign in / Sign up

Export Citation Format

Share Document