scholarly journals Multidistribution Center Location Based on Real-Parameter Quantum Evolutionary Clustering Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Huaixiao Wang ◽  
Wanhong Zhu ◽  
Jianyong Liu ◽  
Ling Li ◽  
Zhuchen Yin
Keyword(s):  
Clustering Algorithm ◽  
High Efficiency ◽  
Real Parameter ◽  
The Real ◽  
Good Convergence ◽  
Evolutionary Clustering ◽  
Simulated Annealing Genetic Algorithm ◽  
Center Location ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering

To determine the multidistribution center location and the distribution scope of the distribution center with high efficiency, the real-parameter quantum-inspired evolutionary clustering algorithm (RQECA) is proposed. RQECA is applied to choose multidistribution center location on the basis of the conventional fuzzy C-means clustering algorithm (FCM). The combination of the real-parameter quantum-inspired evolutionary algorithm (RQIEA) and FCM can overcome the local search defect of FCM and make the optimization result independent of the choice of initial values. The comparison of FCM, clustering based on simulated annealing genetic algorithm (CSAGA), and RQECA indicates that RQECA has the same good convergence as CSAGA, but the search efficiency of RQECA is better than that of CSAGA. Therefore, RQECA is more efficient to solve the multidistribution center location problem.

A novel method for spacecraft electrical fault detection based on FCM clustering and WPSVM classification with PCA feature extraction

2016 ◽  
Vol 231 (1) ◽  
pp. 98-108 ◽  
Author(s):  
Ke Li ◽  
Yalei Wu ◽  
Shimin Song ◽  
Yi sun ◽  
Jun Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
High Efficiency ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Electrical Characteristics ◽  
Complex Signals ◽  
Fcm Clustering

The measurement of spacecraft electrical characteristics and multi-label classification issues are generally including a large amount of unlabeled test data processing, high-dimensional feature redundancy, time-consumed computation, and identification of slow rate. In this paper, a fuzzy c-means offline (FCM) clustering algorithm and the approximate weighted proximal support vector machine (WPSVM) online recognition approach have been proposed to reduce the feature size and improve the speed of classification of electrical characteristics in the spacecraft. In addition, the main component analysis for the complex signals based on the principal component feature extraction is used for the feature selection process. The data capture contribution approach by using thresholds is furthermore applied to resolve the selection problem of the principal component analysis (PCA), which effectively guarantees the validity and consistency of the data. Experimental results indicate that the proposed approach in this paper can obtain better fault diagnosis results of the spacecraft electrical characteristics’ data, improve the accuracy of identification, and shorten the computing time with high efficiency.

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

An Improved Fuzzy C-Means Clustering Algorithm and Application in Meteorological Data

2011 ◽  
Vol 181-182 ◽  
pp. 545-550
Author(s):  
Hong Fei Li ◽  
Fu Ling Wang ◽  
Shi Jue Zheng ◽  
Li Gao
Keyword(s):  
Clustering Algorithm ◽  
Meteorological Data ◽  
Observation Data ◽  
Fuzzy Clustering Algorithm ◽  
Temperature Observation ◽  
Degree Of Membership ◽  
Traditional Algorithm ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Clustering Effect

The fuzzy clustering algorithm is sensitive to the m value and the degree of membership. Because of the deficiencies of traditional FCM clustering algorithm, we made specific improvement. Through the calculation of the value of m, the amendments of degree of membership to the discussion of issues, effectively compensate for the deficiencies of the traditional algorithm and achieve a relatively good clustering effect. Finally, through the analysis of temperature observation data of the three northeastern province of china in 2000, the reasonableness of the method is verified.

FCMLSM Segmentation of Micro-Vessels in Slight Defocused Microscopic Images

2019 ◽  
Vol 23 (6) ◽  
pp. 1073-1079
Author(s):  
Zhongming Luo ◽  
Yu Zhang ◽  
Zixuan Zhou ◽  
Xuan Bi ◽  
Haibin Wu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Clustering Algorithm ◽  
Evolution Equations ◽  
High Efficiency ◽  
Level Set Function ◽  
Level Set Segmentation ◽  
Segmentation Algorithms ◽  
Fcm Clustering ◽  
Set Function

To address problems relating to microscopic micro-vessel images of living bodies, including poor vessel continuity, blurry boundaries between vessel edges and tissue and uneven field illuminance, and this paper put forward a fuzzy-clustering level-set segmentation algorithm. By this method, pre-treated micro-vessel images were segmented by the fuzzy c-means (FCM) clustering algorithm to obtain original contours of interesting areas in images. By the evolution equations of the improved level set function, accurate segmentation of microscopic micro-vessel images was realized. This method can effectively solve the problem of manual initialization of contours, avoid the sensitivity to initialization and improve the accuracy of level-set segmentation. The experiment results indicate that compared with traditional micro-vessel image segmentation algorithms, this algorithm is of high efficiency, good noise immunity and accurate image segmentation.

scholarly journals Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2085
Author(s):  
Ranjita Rout ◽  
Priyadarsan Parida ◽  
Youseef Alotaibi ◽  
Saleh Alghamdi ◽  
Osamah Ibrahim Khalaf
Keyword(s):  
Skin Lesion ◽  
Clustering Algorithm ◽  
Skin Lesions ◽  
Gaussian Filter ◽  
Watershed Transform ◽  
Fuzzy C Means ◽  
Local Variance ◽  
Variance Filter ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering

Early identification of melanocytic skin lesions increases the survival rate for skin cancer patients. Automated melanocytic skin lesion extraction from dermoscopic images using the computer vision approach is a challenging task as the lesions present in the image can be of different colors, there may be a variation of contrast near the lesion boundaries, lesions may have different sizes and shapes, etc. Therefore, lesion extraction from dermoscopic images is a fundamental step for automated melanoma identification. In this article, a watershed transform based on the fast fuzzy c-means (FCM) clustering algorithm is proposed for the extraction of melanocytic skin lesion from dermoscopic images. Initially, the proposed method removes the artifacts from the dermoscopic images and enhances the texture regions. Further, it is filtered using a Gaussian filter and a local variance filter to enhance the lesion boundary regions. Later, the watershed transform based on MMLVR (multiscale morphological local variance reconstruction) is introduced to acquire the superpixels of the image with accurate boundary regions. Finally, the fast FCM clustering technique is implemented in the superpixels of the image to attain the final lesion extraction result. The proposed method is tested in the three publicly available skin lesion image datasets, i.e., ISIC 2016, ISIC 2017 and ISIC 2018. Experimental evaluation shows that the proposed method achieves a good result.

FUZZY C-MEANS CLUSTERING ALGORITHM WITH TWO LAYERS AND ITS APPLICATION TO IMAGE SEGMENTATION BASED ON TWO-DIMENSIONAL HISTOGRAM

1994 ◽  
Vol 02 (03) ◽  
pp. 343-350 ◽  
Author(s):  
WEIXIN XIE ◽  
JIANZHUANG LIU
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Two Dimensional ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Hard Clustering

This paper presents a fast fuzzy c-means (FCM) clustering algorithm with two layers, which is a mergence of hard clustering and fuzzy clustering. The result of hard clustering is used to initialize the c cluster centers in fuzzy clustering, and then the number of iteration steps is reduced. The application of the proposed algorithm to image segmentation based on the two dimensional histogram is provided to show its computational efficience.

Adaptive Kernel-Based Fuzzy C-Means Clustering with Spatial Constraints for Image Segmentation

2018 ◽  
Vol 33 (01) ◽  
pp. 1954003 ◽  
Author(s):  
Guang Hu ◽  
Zhenbin Du
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Spatial Constraints ◽  
Fuzzy C Means ◽  
Central Pixel ◽  
Fuzzy C Means Clustering ◽  
Adaptive Kernel ◽  
Fcm Clustering ◽  
Image Pixels

In order to resolve the disadvantages of fuzzy C-means (FCM) clustering algorithm for image segmentation, an improved Kernel-based fuzzy C-means (KFCM) clustering algorithm is proposed. First, the reason why the kernel function is introduced is researched on the basis of the classical KFCM clustering. Then, using spatial neighborhood constraint property of image pixels, an adaptive weighted coefficient is introduced into KFCM to control the influence of the neighborhood pixels to the central pixel automatically. At last, a judging rule for partition fuzzy clustering numbers is proposed that can decide the best clustering partition numbers and provide an optimization foundation for clustering algorithm. An adaptive kernel-based fuzzy C-means clustering with spatial constraints (AKFCMS) model for image segmentation approach is proposed in order to improve the efficiency of image segmentation. Various experiment results show that the proposed approach can get the spatial information features of an image accurately and is robust to realize image segmentation.

Improved Fuzzy C-Means Based on the Optimal Number of Clusters

2013 ◽  
Vol 392 ◽  
pp. 803-807 ◽  
Author(s):  
Xue Bo Feng ◽  
Fang Yao ◽  
Zhi Gang Li ◽  
Xiao Jing Yang
Keyword(s):  
Convergence Rate ◽  
Clustering Algorithm ◽  
Optimal Number ◽  
Data Set ◽  
Number Of Clusters ◽  
Fuzzy C Means ◽  
Initial Cluster ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Optimal Number Of Clusters

According to the number of cluster centers, initial cluster centers, fuzzy factor, iterations and threshold, Fuzzy C-means clustering algorithm (FCM) clusters the data set. FCM will encounter the initialization problem of clustering prototype. Firstly, the article combines the maximum and minimum distance algorithm and K-means algorithm to determine the number of clusters and the initial cluster centers. Secondly, the article determines the optimal number of clusters with Silhouette indicators. Finally, the article improves the convergence rate of FCM by revising membership constantly. The improved FCM has good clustering effect, enhances the optimized capability, and improves the efficiency and effectiveness of the clustering. It has better tightness in the class, scatter among classes and cluster stability and faster convergence rate than the traditional FCM clustering method.

Hybrid Genetic Clustering by Using FCM and Geodesic Distance for Complex Distributed Data

2012 ◽  
Vol 263-266 ◽  
pp. 2597-2601 ◽  
Author(s):  
Yong Sheng Yang ◽  
Gang Li ◽  
Yong Sheng Zhu ◽  
You Yun Zhang
Keyword(s):  
Clustering Algorithm ◽  
Geodesic Distance ◽  
Computational Time ◽  
Distributed Data ◽  
Distance Metric ◽  
Fitness Evaluation ◽  
Benchmark Datasets ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Genetic Clustering

To efficiently find hidden clusters in datasets with complex distributed data，inspired by complementary strategies, a hybrid genetic clustering algorithm was developed, which is on the basis of the geodesic distance metric, and combined with the Fuzzy C-Means clustering (FCM) algorithm. First, instead of using Euclidean distance，the new approach employs geodesic distance based dissimilarity metric during all fitness evaluation. And then, with the help of FCM clustering, some sub-clusters with spherical distribution are partitioned effectively. Next, a genetic algorithm based clustering using geodesic distance metric, named GCGD, is adopted to cluster the clustering centers obtained from FCM clustering. Finally, the final results are acquired based on above two clustering results. Experimental results on eight benchmark datasets clustering questions show the effectiveness of the algorithm as a clustering technique. Compared with conventional GCGD, the hybrid clustering can decrease the computational time obviously, while retaining high clustering correct ratio.

scholarly journals Prediction of Depth of Seawater Using Fuzzy C-Means Clustering Algorithm of Crowdsourced SONAR Data

2021 ◽  
Vol 13 (11) ◽  
pp. 5823
Author(s):  
Ahmadhon Akbarkhonovich Kamolov ◽  
Suhyun Park
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Machine Learning Algorithms ◽  
Human Beings ◽  
Fuzzy C Means ◽  
Sonar Sensors ◽  
Crowdsourced Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering

Implementing AI in all fields is a solution to the complications that can be troublesome to solve for human beings and will be the key point of the advancement of those spheres. In the marine world, specialists also encounter some problems that can be revealed through addressing AI and machine learning algorithms. One of these challenges is determining the depth of the seabed with high precision. The depth of the seabed is utterly significant in the procedure of ships at sea occupying a safe route. Thus, it is considerably crucial that the ships do not sit in shallow water. In this article, we have addressed the fuzzy c-means (FCM) clustering algorithm, which is one of the vigorous unsupervised learning methods under machine learning to solve the mentioned problems. In the case study, crowdsourced data have been trained, which are gathered from vessels that have installed sound navigation and ranging (SONAR) sensors. The data for the training were collected from ships sailing in the south part of South Korea. In the training section, we segregated the training zone into the diminutive size areas (blocks). The data assembled in blocks had been trained in FCM. As a result, we have received data separated into clusters that can be supportive to differentiate data. The results of the effort show that FCM can be implemented and obtain accurate results on crowdsourced bathymetry.

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