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.

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.

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.

scholarly journals Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3285 ◽  
Author(s):  
Hang Zhang ◽  
Jian Liu ◽  
Lin Chen ◽  
Ning Chen ◽  
Xiao Yang
Keyword(s):  
Surface Roughness ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Roughness Measurement ◽  
Surface Roughness Measurement ◽  
Fuzzy Clustering Algorithm ◽  
Fuzzy C Means Clustering ◽  
Highly Correlated

Due to the limitation of the fixed structures of neighborhood windows, the quality of spatial information obtained from the neighborhood pixels may be affected by noise. In order to compensate this drawback, a robust fuzzy c-means clustering with non-neighborhood spatial information (FCM_NNS) is presented. Through incorporating non-neighborhood spatial information, the robustness performance of the proposed FCM_NNS with respect to the noise can be significantly improved. The results indicate that FCM_NNS is very effective and robust to noisy aliasing images. Moreover, the comparison of other seven roughness indexes indicates that the proposed FCM_NNS-based F index can characterize the aliasing degree in the surface images and is highly correlated with surface roughness (R2 = 0.9327 for thirty grinding samples).

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.

scholarly journals Implementation of the Fuzzy C-Means Clustering Algorithm in Meteorological Data

2013 ◽  
Vol 6 (6) ◽  
pp. 1-18 ◽  
Author(s):  
Yinghua Lu ◽  
Tinghuai Ma ◽  
Changhong Yin ◽  
Xiaoyu Xie ◽  
Wei Tian ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Meteorological Data ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

scholarly journals The Bi-Level Optimal Configuration Model of the CCHP System Based on the Improved FCM Clustering Algorithm

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 907
Author(s):  
Kaoshe Zhang ◽  
Peiji Feng ◽  
Gang Zhang ◽  
Tuo Xie ◽  
Jinwang Hou ◽  
...  
Keyword(s):  
Carbon Dioxide Emissions ◽  
Clustering Algorithm ◽  
Meteorological Data ◽  
Operating Cost ◽  
Optimal Configuration ◽  
Configuration Model ◽  
Configuration Optimization ◽  
Nsga Ii ◽  
Fcm Algorithm ◽  
Fcm Clustering

To improve the comprehensive benefits of the CCHP system, this paper proposes a bi-level optimal configuration model of the CCHP system based on the improved FCM clustering algorithm. Firstly, based on the traditional FCM clustering algorithm, the entropy method is used to introduce the PFS index and the Vp index in a weighted form to achieve a comprehensive evaluation of the clustering effect. The effectiveness of the improved FCM algorithm is verified by analyzing the clustering process of the load and meteorological data using the improved FCM algorithm. Then the best cluster number and fuzzy coefficient is found using the traversal method. Secondly, a bi-level configuration optimization model is constructed. The outer layer is the configuration optimization layer, and the inner layer is the operation optimization layer. The model is solved by combining the NSGA-II and PSO algorithms. Finally, a bi-level optimal configuration model is constructed for actual cases, and the clustering results of the improved FCM algorithm are brought into the model. The example calculation analyses show that, compared with existing methods, the proposed method significantly reduces the operating cost and carbon dioxide emissions of the CCHP microgrid.

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.

Drilling Wear Recognition Based on Fuzzy C-Means Clustering Algorithm

2012 ◽  
Vol 538-541 ◽  
pp. 1408-1412
Author(s):  
Ming Xia Yan
Keyword(s):  
Clustering Algorithm ◽  
Fuzzy C Means ◽  
Degree Of Membership ◽  
Fuzzy C Means Clustering ◽  
Clustering And Classification ◽  
Simulation Results ◽  
Severe Degree ◽  
Fuzzy Description ◽  
Matlab Programming ◽  
Wear Modes

Fuzzy c-means clustering algorithm was introduced in detail to classify a set of original sampling data on drilling wear in this paper. Simulation results by Matlab programming show that drill wear modes can be successfully represented by four fuzzy grades after fuzzy clustering and classification. The analysis result indicates that fuzzy description can properly reflect drill wear, FCM can effectively identify different wear modes. It is suggested that the severe degree of membership of wear be used as a criterion for replacement of a drill. This technique is simple and is adaptable to different environment in automatic manufacturing

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