A Fixed Suppressed Rate Selection Method for Suppressed Fuzzy C-Means Clustering Algorithm

Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better classification performance of fuzzy c-means clustering algorithm had been studied by many researchers and applied in many fields. In the algorithm, how to select the suppressed rate is a key step. In this paper, we give a method to select the fixed suppressed rate by the structure of the data itself. The experimental results show that the proposed method is a suitable way to select the suppressed rate in suppressed fuzzy c-means clustering algorithm.

Hybrid Clustering Using Firefly Optimization and Fuzzy C-Means Algorithm

Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis of the increasing data. The Firefly Algorithm (FA) is one of the bio-inspired algorithms and it is recently used to solve the clustering problems. In this paper, Hybrid F-Firefly algorithm is developed by combining the Fuzzy C-Means (FCM) with FA to improve the clustering accuracy with global optimum solution. The Hybrid F-Firefly algorithm is developed by incorporating FCM operator at the end of each iteration in FA algorithm. This proposed algorithm is designed to utilize the goodness of existing algorithm and to enhance the original FA algorithm by solving the shortcomings in the FCM algorithm like the trapping in local optima and sensitive to initial seed points. In this research work, the Hybrid F-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets. From the experimental results, it is observed that the Hybrid F-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm.

Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection

The premise and basis of load modeling are substation load composition inquiries and cluster analyses.However,the traditional kernel fuzzy C-means(KFCM)algorithm is limited by artificial clustering number selection and its convergence to local optimal solutions.To overcome these limitations,an improved KFCM algorithm with adaptive optimal clustering number selection is proposed in this paper.This algorithm optimizes the KFCM algorithm by combining the powerful global search ability of genetic algorithm and the robust local search ability of simulated annealing algorithm.The improved KFCM algorithm adaptively determines the ideal number of clusters using the clustering evaluation index ratio.Compared with the traditional KFCM algorithm,the enhanced KFCM algorithm has robust clustering and comprehensive abilities,enabling the efficient convergence to the global optimal solution.

A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.

Research and Implementation of the Enterprise Evaluation Based on a Fusion Clustering Model of AHP-FCM

Traditional clustering method is easy to slow convergence speed because of high data dimension and setting random initial clustering center. To improve these problems, a novel method combining subtractive clustering with fuzzy C-means( FCM)clustering will be advanced. In the method, the initial cluster number and cluster center can be obtained using subtractive clustering. On this basis,clustering result will be further optimized with FCM. In addition,the data dimension will be reduced through the analytic hierarchy process( AHP) before clustering calculating.In order to verify the effectiveness of fusion algorithm,an example about enterprise credit evaluation will be carried out. The results show that the fusion clustering algorithm is suitable for classifying high-dimension data,and the algorithm also does well in running up processing speed and improving visibility of result. So the method is suitable to promote the use.

Agent Based Segmentation of the MRI Brain Using a Robust C-Means Algorithm

In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of the most active research fields in the medical imaging domain. Because of the fuzzy nature of the MRI images, many researchers have adopted the fuzzy clustering approach to segment them. In this work, a fast and robust multi-agent system (MAS) for MRI segmentation of the brain is proposed. This system gets its robustness from a robust c-means algorithm (RFCM) and obtains its fastness from the beneficial properties of agents, such as autonomy, social ability and reactivity. To show the efficiency of the proposed method, we test it on a normal brain brought from the BrainWeb Simulated Brain Database. The experimental results are valuable in both robustness to noise and running times standpoints.

Fault Pattern Recognition based on Kernel Method and Fuzzy C-means

A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means （FCM） is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery.

Abnormal State Detection of OLTC Based on Improved Fuzzy C-means Clustering

An accurate extraction of vibration signal characteristics of an on-load tap changer(OLTC)during contact switching can effectively help detect its abnormal state.Therefore,an improved fuzzy C-means clustering method for abnormal state detection of the OLTC contact is proposed.First,the wavelet packet and singular spectrum analysis are used to denoise the vibration signal generated by the moving and static contacts of the OLTC.Then,the Hilbert-Huang transform that is optimized by the ensemble empirical mode decomposition(EEMD)is used to decompose the vibration signal and extract the boundary spectrum features.Finally,the gray wolf algorithm-based fuzzy C-means clustering is used to denoise the signal and determine the abnormal states of the OLTC contact.An analysis of the experimental data shows that the proposed secondary denoising method has a better denoising effect compared to the single denoising method.The EEMD can improve the modal aliasing effect,and the improved fuzzy C-means clustering can effectively identify the abnormal state of the OLTC contacts.The analysis results of field measured data further verify the effectiveness of the proposed method and provide a reference for the abnormal state detection of the OLTC.

A NEW UNSUPERVISED CLASSIFICATION ALGORITHM FOR POLARIMETRIC SAR IMAGES BASED ON FUZZY SET THEORY

In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combi-nation of the usage of polarimetric information of SAR images and the unsupervised classification method based on fuzzy set theory. Image quantization and image enhancement are used to preprocess the POLSAR data. Then the polarimetric information and Fuzzy C-Means (FCM) clustering algorithm are used to classify the preprocessed images. The advantages of this algorithm are the automated classification, its high classifica-tion accuracy, fast convergence and high stability. The effectiveness of this algorithm is demonstrated by ex-periments using SIR-C/X-SAR (Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar) data.

基于FCM算法的多属性分析技术在河道砂体精细刻画中的应用——以西湖凹陷T气田为例

西湖凹陷T气田经过十多年的勘探与开发,亟需在主力层花港组内寻找潜力目标。该区为浅水三角洲沉积体系,岩性组合在空间上变化快,为了精确识别河道砂体及其边界,在海上少井条件下利用三维地震资料识别并刻画河道砂体。在等时地层划分的基础上,对目的层段进行岩石物理性质分析,通过地震沉积学的技术方法结合岩芯及测井等资料,对沉积微相做出初步判断,在此基础上提取6类48种地震属性,与砂厚及各属性之间进行相关性分析,对地震属性进行优选,将优选出的3种反映地质体边界、岩性较好的地震属性采用基于模糊C-均值(FCM)算法的多属性聚类分析,以达到数据降维、减少冗余的效果,研究分流河道沉积体系的整体展布规律。再进行多属性RGB融合显示,增强河道砂体边界的刻画,结合构造特征以及预测的砂体厚度综合分析,提出有利目标区,为后续油田滚动开发及井位部署提供依据。

Employment Quality EvaluationModel Based on Hybrid Intelligent Algorithm

In order to solve the defect of large error in current employment quality evaluation,an employment quality evaluation model based on grey correlation degree method and fuzzy C-means(FCM)is proposed.Firstly,it analyzes the related research work of employment quality evaluation,establishes the employment quality evaluation index system,collects the index data,and normalizes the index data;Then,the weight value of employment quality evaluation index is determined by Grey relational analysis method,and some unimportant indexes are removed;Finally,the employment quality evaluation model is established by using fuzzy cluster analysis algorithm,and compared with other employment quality evaluation models.The test results show that the employment quality evaluation accuracy of the design model exceeds 93%,the employment quality evaluation error can meet the requirements of practical application,and the employment quality evaluation effect is much better than the comparison model.The comparison test verifies the superiority of the model.

一种基于三角模糊数多指标信息的FCM聚类算法

针对一类具有不确定性三角模糊数多指标信息的聚类分析问题,基于传统的数值信息FCM聚类算法,提出一种新的聚类分析算法.首先描述了具有三角模糊数多指标信息的聚类分析问题,提出并证明了基于三角模糊数多指标信息的关于最优划分和最优聚类中心确定的两个定理;然后根据这两个定理,进一步给出了基于三角模糊数信息的FCM聚类算法的迭代步骤;最后通过一个算例说明了该聚类算法的具体应用.

模糊C-均值(FCM)聚类算法的实现

传统的FCM算法能够将靠近边界的具有固有形状的两个簇合并成为一个大的簇。然而,对于一些稍微复杂的数据,如果没有其它的像去除小簇之类的机制的话,FCM算法很难将非常接近的类聚类到一起。给出的聚类算法是在传统FCM算法的循环之后添加了去除掉空簇的步骤,解决了上述很难将非常接近的类聚到一个簇中的问题。另外,为便于选出最优结果,在递归之后又添加了计算聚类有效性的步骤。最后用Java实现了该算法并在数据集上进行了实验,证实了改进方法的有效性。

半监督FCM聚类算法目标函数研究

分析了现有半监督FCM算法目标函数的物理意义和平衡系数α的选取,说明Stutz对Pedrycz目标函数的修改使半监督的物理意义更清楚,它在α=1,0时均退化为标准FCM算法,给出了修改后SS-FCM算法的交替求解过程。实验结果:(1)修改算法与Pedrycz算法有相同的半监督作用和清楚的物理解释;(2)对labeled样本采用FCM算法赋值比用随机数的收敛稳定性高;(3)优选的少量labeled样本,使用模糊协方差的SS-CFCM算法提高了聚类准确性和收敛速度。

一种有效的FCM算法的实现方式

提出了一种有效的FCM算法的实现方式。目前FCM算法的实现大多采用启发式方法,根据经验或实验用人工选择的方法来确定FCM算法中的所有参数。利用一个有关选择权指数m的新的研究结果,提出了一种有效的FCM算法的实现方式,选择了一种简便的聚类有效性函数用于聚类结果的检验,最后用数值实验验证了方法的合理性。

一种基于区间数多指标信息的FCM聚类算法

针对一类具有不确定性区间数多指标信息的聚类分析问题,基于传统的数值信息FCM(fuzzyc＿means)聚类算法,提出了一种新的聚类分析算法.首先描述了具有区间数多指标信息的聚类分析问题,其次提出并证明了基于区间数多指标信息的关于最优划分和最优聚类中心确定的两个定理.然后根据提出的两个定理,进一步给出了基于区间数信息的FCM聚类算法的迭代步骤.最后,通过一个算例说明了给出的聚类算法.

改进的FCM半监督聚类算法

通过将类间分离度函数引入到模糊C-均值聚类算法中,结合半监督的思想,建立基于信息熵的半监督模糊C-均值聚类模型,并对该模型的求解过程进行推导,提出一种新的算法.为了验证算法的有效性,将该算法在UCI数据集上进行实验,实验结果表明,该算法比仅引入信息熵的模糊C-均值聚类方法聚类性能更好.

基于直方图相关性约束的快速多阈值FCM图像分割算法

针对传统的模糊C均值(FCM)聚类算法在样本数和特征数较多时,运算较为复杂以及耗时较多的问题,本文提出了一种采用直方图的相关性作为约束采样率的快速多阈值FCM分割方法,控制图像失真,使得需要运算的数据量减少,以获得较快的分割速度。由于借助了基于模糊集的图像分割技术——模糊C均值算法实现多阈值图像分割,考虑到了每个像素对于聚类中心的隶属度,使得其有较好的适用性。根据实验结果,在保持传统FCM算法的分割效果的前提下,该算法的分割灰度图像耗时是传统FCM的1.4%,因此该算法具有一定的应用价值。

一种基于区间数多指标信息的FCM聚类算法

针对一类具有不确定性区间数多指标信息的聚类分析问题,依据传统的基于数值信息的FCM聚类算法的思路,提出了一种新的聚类分析算法。文章首先描述了具有区间数多指标信息的聚类分析问题;其次给出了基于区间数多指标信息的关于最优划分和最优聚类中心确定的两个定理;然后给出了基于区间数多指标信息的FCM聚类算法的计算步骤。该算法的特点是聚类中心的表现形式为精确的数值,给出的两个定理说明了该聚类算法的收敛性。最后,通过给出一个算例说明了本文给出的聚类算法。

基于MS-FCM算法的MR图像分割方法

针对传统模糊C-均值(FCM)聚类算法在分割低信噪比图像时准确性较差的问题,提出一种用于MR图像分割的改进算法MS-FCM。针对脑部MR图像相邻像素属于同一分类的模糊隶属度相近的特性,在迭代过程中对隶属度数据集进行滤波,以降低噪声对聚类精度的影响。模拟脑部MR图像和临床脑部MR图像的分割实验证明,该算法可以提高图像分割精度。