AN UNSUPERVISED KERNEL BASED FUZZY C-MEANS CLUSTERING ALGORITHM WITH KERNEL NORMALISATION

2004 ◽  
Vol 04 (04) ◽  
pp. 355-373 ◽  
Author(s):  
SHANG-MING ZHOU ◽  
JOHN Q. GAN
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Experimental Results ◽  
New Method ◽  
Fcm Algorithm ◽  
Input Space ◽  
Fuzzy C Means ◽  
Benchmark Datasets ◽  
Fuzzy C Means Clustering ◽  
Mercer Kernel

In this paper, a novel procedure for normalising Mercer kernel is suggested firstly. Then, the normalised Mercer kernel techniques are applied to the fuzzy c-means (FCM) algorithm, which leads to a normalised kernel based FCM (NKFCM) clustering algorithm. In the NKFCM algorithm, implicit assumptions about the shapes of clusters in the FCM algorithm is removed so that the new algorithm possesses strong adaptability to cluster structures within data samples. Moreover, a new method for calculating the prototypes of clusters in input space is also proposed, which is essential for data clustering applications. Experimental results on several benchmark datasets have demonstrated the promising performance of the NKFCM algorithm in different scenarios.

scholarly journals Single-Valued Neutrosophic Clustering Algorithm Based on Tsallis Entropy Maximization

Axioms ◽  
2018 ◽  
Vol 7 (3) ◽  
pp. 57 ◽  
Author(s):  
Qiaoyan Li ◽  
Yingcang Ma ◽  
Florentin Smarandache ◽  
Shuangwu Zhu
Keyword(s):  
Objective Function ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Real Life ◽  
Neutrosophic Set ◽  
Entropy Maximization ◽  
Fuzzy C Means ◽  
Promising Tool ◽  
Life Problems ◽  
Benchmark Datasets

Data clustering is an important field in pattern recognition and machine learning. Fuzzy c-means is considered as a useful tool in data clustering. The neutrosophic set, which is an extension of the fuzzy set, has received extensive attention in solving many real-life problems of inaccuracy, incompleteness, inconsistency and uncertainty. In this paper, we propose a new clustering algorithm, the single-valued neutrosophic clustering algorithm, which is inspired by fuzzy c-means, picture fuzzy clustering and the single-valued neutrosophic set. A novel suitable objective function, which is depicted as a constrained minimization problem based on a single-valued neutrosophic set, is built, and the Lagrange multiplier method is used to solve the objective function. We do several experiments with some benchmark datasets, and we also apply the method to image segmentation using the Lena image. The experimental results show that the given algorithm can be considered as a promising tool for data clustering and image processing.

A Novel Segmentation Method for Brain MRI Using a Block-Based Integrated Fuzzy C-Means Clustering Algorithm

2020 ◽  
Vol 10 (3) ◽  
pp. 579-585
Author(s):  
Hui Zhang ◽  
Hongjie Zhang
Keyword(s):  
Clustering Algorithm ◽  
Brain Diseases ◽  
Cluster Center ◽  
Large Sample Size ◽  
Large Sample ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Final Cluster ◽  
Block Based

Accurate segmentation of brain tissue has important guiding significance and practical application value for the diagnosis of brain diseases. Brain magnetic resonance imaging (MRI) has the characteristics of high dimensionality and large sample size. Such datasets create considerable computational complexity in image processing. To efficiently process large sample data, this article integrates the proposed block clustering strategy with the classic fuzzy C-means clustering (FCM) algorithm and proposes a block-based integrated FCM clustering algorithm (BI-FCM). The algorithm first performs block processing on each image and then clusters each subimage using the FCM algorithm. The cluster centers for all subimages are again clustered using FCM to obtain the final cluster center. Finally, the distance from each pixel to the final cluster center is obtained, and the corresponding division is performed according to the distance. The dataset used in this experiment is the Simulated Brain Database (SBD). The results show that the BI-FCM algorithm addresses the large sample processing problem well, and the theory is simple and effective.

Simulation and Application on Data Clustering Based on an Improved FCM Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 1589-1592
Author(s):  
Xiang Ping Hu
Keyword(s):  
Fault Diagnosis ◽  
Differential Evolution ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
New Method ◽  
Initial Value ◽  
Fcm Algorithm ◽  
Chaotic Mapping

An improved data clustering algorithm was proposed based on the Fuzzy C-Means (FCM) algorithm for the purpose of clustering the data precisely and effectively, through progressing the performance of the data clustering to afford the element work for the application of fault diagnosis and target recognition and so on. There was fatal weakness for the traditional FCM algorithm that the algorithm is sensitive to initial value and noise. The chaotic differential evolution FCM algorithm was proposed according to the efficient global search capability of differential evolution algorithm and the traversal characteristic of chaotic time series. The improved algorithm used the Logistics chaotic mapping to search for the optimal solution, and the chaos disturbance was introduced into the evolutionary population to make up for the defects of FCM algorithm. The new method can overcome the problems of initial value sensitiveness with FCM and local convergence with genetic algorithm. Because the new method. Three types of typical vibration data of faults engines was taken as the example for the research and application. The simulation and application result shows that the data clustering performance of the improved FCM algorithm is much better than the traditional FCM algorithm, and the accuracy rates of fault diagnosis in the application was increased by more than twenty percent, it shows good application prospect.

An Interval Slope Approach to Fuzzy C-Means Clustering Algorithm for Interval Valued Data

2014 ◽  
Vol 989-994 ◽  
pp. 1641-1645
Author(s):  
Yan Jin ◽  
Jiang Hong Ma
Keyword(s):  
Clustering Algorithm ◽  
Interval Data ◽  
Experimental Results ◽  
Clustering Methods ◽  
Structure Information ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Interval Valued

Interval data is a range of continuous values which can describe the uncertainty. The traditional clustering methods ignore the structure information of intervals. And some modified ones have been developed. We have already used Taylor technique to perform well in the fuzzy c-means clustering algorithm. In this paper, we propose a new way based on the mixed interval slopes technique and interval computing. Experimental results also show the effectiveness of our approach.

An Improved Suppressed FCM Algorithm for Image Segmentation

2013 ◽  
Vol 712-715 ◽  
pp. 2349-2353
Author(s):  
Hong Lan ◽  
Shao Bin Jin
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Experimental Results ◽  
Two Dimensional ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Neighborhood Information ◽  
Noisy Images ◽  
Fuzzy C Means Clustering ◽  
Improved Algorithm

Fuzzy C-Means clustering(FCM) algorithm plays an important role in image segmentation, but it is sensitive to noise because of not taking into account the spatial information. Addressing this problem, this paper presents an improved suppressed FCM algorithm based on the pixels and the spatial neighborhood information of the image. The algorithm combines the two-dimentional histogram and suppressed FCM algorithm together. First, construct a two-dimentional histogram instead of one-dimentional histogram, which can better distinguish the distribution of the object and background for noisy images. Then determine the initial clustering based on two-dimensional histogram. Last, provide a new way to determine the suppressed factor and use the improved FCM algorithm to realize the image segmentation. Experimental results show that the improved algorithm is effective to improve the clustering speed, and can achieve better segmentation results.

Improvement on a Fuzzy C-Means Algorithm Based on Genetic Algorithm

2014 ◽  
Vol 614 ◽  
pp. 385-388
Author(s):  
Guo Chen Jiang ◽  
Zhi Jian Sun
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Optimal Choice ◽  
Experimental Results ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Fcm Clustering ◽  
Fuzzy C Means Algorithm

Weighting exponent m is an important parameter in fuzzy c-means(FCM) algorithm. In this paper, an approach based on genetic algorithm is proposed to improve the FCM clustering algorithm through the optimal choice of the parameter m. Experimental results show that the better clustering results are obtained through the new algorithm.

scholarly journals A Weight Possibilistic Fuzzy C-Means Clustering Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiashun Chen ◽  
Hao Zhang ◽  
Dechang Pi ◽  
Mehmed Kantardzic ◽  
Qi Yin ◽  
...  
Keyword(s):  
Data Analysis ◽  
Stock Market ◽  
Objective Function ◽  
Clustering Algorithm ◽  
Lagrange Equation ◽  
Experimental Results ◽  
Retail Market ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Fuzzy C-means (FCM) is an important clustering algorithm with broad applications such as retail market data analysis, network monitoring, web usage mining, and stock market prediction. Especially, parameters in FCM have influence on clustering results. However, a lot of FCM algorithm did not solve the problem, that is, how to set parameters. In this study, we present a kind of method for computing parameters values according to role of parameters in the clustering process. New parameters are assigned to membership and typicality so as to modify objective function, on the basis of which Lagrange equation is constructed and iterative equation of membership is acquired, so does the typicality and center equation. At last, a new possibilistic fuzzy C-means based on the weight parameter algorithm (WPFCM) was proposed. In order to test the efficiency of the algorithm, some experiments on different datasets are conducted to compare WPFCM with FCM, possibilistic C-means (PCM), and possibilistic fuzzy C-means (PFCM). Experimental results show that iterative times of WPFCM are less than FCM about 25% and PFCM about 65% on dataset X12. Resubstitution errors of WPFCM are less than FCM about 19% and PCM about 74% and PFCM about 10% on the IRIS dataset.

A Fuzzy Clustering Algorithm of Automatic Classification Based on EnFCM

2014 ◽  
Vol 989-994 ◽  
pp. 1489-1492 ◽  
Author(s):  
Hong Wei Han ◽  
Lin Tian ◽  
Jia Qing Miao
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Automatic Classification ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Algorithm ◽  
Detection Technology ◽  
Fuzzy C Means Clustering ◽  
Point Detection

Fuzzy c-means (FCM) algorithm is an unsupervised clustering algorithm for image segmentation, and has been widely applied because the segmentation results are consistent with human visual characteristics. Enhanced fuzzy c-means clustering (EnFCM) algorithm is the improved FCM algorithm, which reduces the computational complexity. But, both FCM algorithm and EnFCM algorithm, clustering number still need to be manually determined. This paper, in order to realize the automation degree of algorithm, presents an improved algorithm. It first analyzes the histogram, then automatically determines the clustering number and peak value of each class through use of the peak point detection technology, finally segments image by using EnFCM algorithm. Experiments show that this method is a kind of faster fuzzy clustering algorithm with automatic classification ability for image segmentation.

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.

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