Fuzzy C-Means Algorithm Automatically Determining Optimal Number of Clusters

In clustering analysis, the key to deciding clustering quality is to determine the optimal number of clusters. At present, most clustering algorithms need to give the number of clusters in advance for clustering analysis of the samples. How to gain the correct optimal number of clusters has been an important topic of clustering validation study. By studying and analyzing the FCM algorithm in this study, an accurate and efficient algorithm used to confirm the optimal number of clusters is proposed for the defects of traditional FCM algorithm. For time and clustering accuracy problems of FCM algorithm and relevant algorithms automatically determining the optimal number of clusters, kernel function, AP algorithm and new evaluation indexes were applied to improve the confirmation of complexity and search the scope of traditional fuzzy C-means algorithm, and evaluation of clustering results. Besides, three groups of contrast experiments were designed with different datasets for verification. The results showed that the improved algorithm improves time efficiency and accuracy to certain degree.     

量子蚁群模糊聚类算法在图像分割中的应用

针对模糊C-均值算法对初始值的依赖,容易陷入局部最优值的缺点,本文提出将量子蚁群算法与FCM聚类算法结合,首先利用量子蚁群算法的全局性和鲁棒性以及快速收敛的优点确定图像的初始聚类中心和聚类个数,再将所得结果作为FCM聚类算法的初始参数,然后用FCM聚类算法对医学图像进行分割。实验结果表明,该方法有效解决了FCM算法对初始参数的依赖,克服了FCM算法及蚁群算法容易陷入局部极值的的缺点,而且在分割速度和精度上得到了较大提高。     

基于Dirichlet过程的无线视频码率变化识别算法

研究了通过对终端视频帧质量的聚类分析来识别无线视频传输中码率变化的方法,以便为无线视频传输过程中视频码率自适应调整提供参考依据.针对经典模糊C均值(FCM)算法和K均值(K-means)算法需要设定聚类数目的问题,提出一种基于荻利克雷过程(DP)的FCM算法——DP-FCM算法.该算法将Dirichlet过程和FCM算法相结合,由视频帧信息权重峰值信噪比(IWPSNR)值使用DP过程混合模型模拟估计出聚类数目,然后进行FCM模糊聚类,通过设定合理的阈值,合并聚类结果相似项,完成视频帧的聚类,从而实现视频传输码率变化的识别.以LIVE视频库为试验数据源,对该算法进行了性能测试.试验结果表明,DP-FCM算法能够在无需设定聚类数目的前提下实现视频传输码率变化的分类识别.     

模糊聚类分析在模糊神经网络结构优化中的应用

研究了模糊聚类分析在多变量模糊神经网络的结构确定中的应用,在传统的模糊C－均值算法的基础上,给出了一个衡量聚类有效性的函数和确定模糊指数的启发式方法,并给出了应用该算法的具体的模糊神经网络模型。     

模拟退火与模糊C-均值聚类相结合的图像分割算法

模糊C-均值(FCM)聚类算法是一种结合无监督聚类和模糊集合概念的图像分割技术,比较有效,但存在着受初始聚类中心和隶属度矩阵影响,可能收敛到局部极小的缺点.将模拟退火算法(SA)与模糊C-均值聚类算法相结合,在合理选择冷却进度表的基础上,依据模糊C-均值聚类算法建立模拟退火算法的目标函数,实现了基于模拟退火的模糊C-均值聚类图像分割算法.实验表明,该方法具有搜索全局最优解的能力,因而可得到很好的图像分割结果.     

Cloud manufacturing resources fuzzy classification based on genetic simulated annealing algorithm

To solve the problem of fuzzy classification of manufacturing resources in a cloud manufacturing environment, a hybrid algorithm based on genetic algorithm (GA), simulated annealing (SA) and fuzzy C-means clustering algorithm (FCM) is proposed. In this hybrid algorithm, classification is based on the processing feature and attributes of the manufacturing resource; the inner and outer layers of the nested loops are solving it, GA obtains the best classification number in the outer layer; the fitness function is constructed by fuzzy clustering algorithm (FCM), carrying out the selection, crossover and mutation operation and SA cooling operation. The final classification results are obtained in the inner layer. Using the hybrid algorithm to solve 45 kinds of manufacturing resources, the optimal classification number is 9 and the corresponding classification results are obtained, proving that the algorithm is effective.     

Automatic Data Clustering Based Mean Best Artificial Bee Colony Algorithm

Fuzzy C-means (FCM) is a clustering method that falls under unsupervised machine learning. The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster centres. Artificial Bee Colony (ABC) is a type of swarm algorithm that strives to improve the members’ solution quality as an iterative process with the utilization of particular kinds of randomness. However, ABC has some weaknesses, such as balancing exploration and exploitation. To improve the exploration process within the ABC algorithm, the mean artificial bee colony (MeanABC) by its modified search equation that depends on solutions of mean previous and global best is used. Furthermore, to solve the main issues of FCM, Automatic clustering algorithm was proposed based on the mean artificial bee colony called (AC-MeanABC). It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value. A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of AC-MeanABC. The experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.     

基于粒子群优化聚类的汽轮机组振动故障诊断

针对模糊C-均值聚类算法(FCM)容易陷入局部极值和对初始值敏感的不足,提出了一种新的模糊聚类算法(PFCM),新算法利用粒子群优化算法(PSO)全局寻优、快速收敛的特点,代替了FCM算法的基于梯度下降的迭代过程,使算法具有很强的全局搜索能力,很大程度上避免了FCM算法易陷入局部极值的缺陷,同时也降低了FCM算法对初始值的敏感度。将该算法应用于汽轮机组振动故障诊断中,与电厂运行实际故障状态对照,仿真结果表明该算法提高了故障诊断的正确率。为汽轮机振动故障诊断方法的研究提供了一种新的思路。     

A rapid automatic brain tumor detection method for MRI images using modified minimum error thresholding technique

This proposed work is aimed to develop a rapid automatic method to detect the brain tumor from T2‐weighted MRI brain images using the principle of modified minimum error thresholding (MET) method. Initially, modified MET method is applied to produce well segmented and sub‐structural clarity for MRI brain images. Further, using FCM clustering the appearance of tumor area is refined. The obtained results are compared with corresponding ground truth images. The quantitative measures of results were compared with the results of those conventional methods using the metrics predictive accuracy (PA), dice coefficient (DC), and processing time. The PA and DC values of the proposed method attained maximum value and processing time is minimum while compared to conventional FCM and k‐means clustering techniques. This proposed method is more efficient and faster than the existing segmentation methods in detecting the tumor region from T2‐weighted MRI brain images. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 77–85, 2015     

基于超像素快速模糊聚类的印刷品图像分割方法

针对当前彩色印刷品色差检测过程中效率低、复杂性高等问题,提出了一种基于超像素快速模糊聚类的印刷品图像分割方法（SFFCM）。先用简单线性迭代聚类（SLIC）算法将图像分割为紧密相邻的超像素区域。每个超像素区域被视为一个独立的聚类单元。随后,将模糊C均值聚类（FCM）算法应用于超像素的归属关系计算中,即引入隶属度值,允许超像素归属于多个聚类中心,并通过权衡归属度值来实现模糊聚类。实验结果表明,相对于其他算法,本文方法在保持良好实时性的同时,实现了较好的分割效果,有效平衡了算法复杂度与分割效果之间的关系。     

空间相关噪声下信源个数的聚类检测算法

针对空间相关噪声情况,利用两个独立阵列之间噪声不相关的特性,采用联合协方差矩阵的规范相关系数作为聚类特征,提出了一种基于模糊c均值聚类的信源个数检测方法.并详细分析了应用Fuzzy-c-Means(FCM)聚类算法进行信源个数检测的3个问题: 聚类的趋势、有效性和聚类中心的初始化.与经典算法相比,本文算法有较好的角度分辨力和检测性能.仿真结果证明了该方法的有效性和鲁棒性.     

基于遗传模糊C-均值聚类算法的除湿机故障诊断

针对除湿机系统的故障诊断问题及其特点,以CFTZ21型除湿机为对象,应用模糊C-均值聚类（FCM）算法进行了研究;引入遗传算法对传统模糊C-均值聚类算法进行了改进,克服了传统算法的不足;结合实验采集到的数据样本,对改进后的遗传模糊C-均值聚类算法进行检验,结果达到预期效果,由此说明,将改进的FCM应用于除湿机故障诊断是可行的。     

基于空间邻域信息的二维模糊聚类图像分割

传统模糊C均值聚类(FCM)算法进行图像分割时仅利用了像素的灰度信息,并且使用对噪声较敏感的欧氏距离作为像素与聚类中心距离度量的标准,因此抗噪性能较差.为了克服传统FCM算法的局限性,本文提出了一种基于空间邻域信息的二维模糊聚类图像分割方法(2DFCM).该方法利用二维直方图描述的像素邻域关系属性,一方面为聚类提供较准确的初始聚类中心,从而避免聚类中的死点问题;另一方面通过提出聚类中心同时在像素值、像素邻域值二维方向上进行更新的思想,建立了包含邻域信息的新的聚类目标函数,实现了图像的分割.实验结果表明,这种方法抗噪能力强、收敛速度快,是一种有效的模糊聚类图像分割方法.     

Agent-based dynamic part family formation for cellular manufacturing applications

One of the main critique on cellular manufacturing and its algorithms is their inability to handle dynamics events, especially dynamic changes in part spectrum. Unfortunately, there are not many efforts in the literature to overcome this problem. Agent oriented computing provides a marvellous opportunity to handle dynamic problems and to provide effective solutions, if carefully and intelligently implemented. In this paper, we have proposed a novel agent-based clustering algorithm for part family formation in cellular manufacturing by considering dynamic demand changes. However, it is not easy to directly compare the performance of the proposed algorithm with the literature results as there is no benchmark for dynamic cell formation problems. We attempt to compare the performance of the present algorithm on static test problems by dynamically introducing parts in these data-sets to our algorithm. Many results have been presented on these static data-sets by utilising several heuristics, meta-heuristics and optimisation-based algorithms. Although the proposed algorithm is not an optimisation-based algorithm and its operation is directed to handle dynamic changes in the problem domain through negotiation, we have shown that it has ability to provide very good results which are comparable to the best known solutions.     

Visual hierarchical clustering of supply chain using growing hierarchical self-organising map algorithm

The study identifies a need for efficient and robust visual clustering approach that can potentially deal with complex supply chain clustering problems. Based on the underlying philosophy of group technology, a growing hierarchical self-organising map algorithm (GHSOM) is proposed to identify a lower two-dimension visual clustering map that can effectively address supply chain clustering problems. The proposed approach provides optimal solutions by decomposing a large-sized supply chain problem into independent, small, manageable problems. It facilitates simple decision-making by exploring similar clusters that are represented by the neighbouring branches in the GHSOM map structure. Unlike other approaches in literature, the proposed approach can further attain good topological ordered representations of the various work order families, to be processed by clusters of supply units along with information on hierarchical sub-cell formation as identifiable from the visually navigable map. The proposed approach has been successfully applied on 16 benchmarked problems. The performance of GHSOM based on grouping efficacy measure outperformed the best results in literature.     

基于太赫兹图像的航天复合材料粘接缺陷检测方法研究

采用频率范围为0.23~0.32THz的反射式连续太赫兹波检测系统,对航天复合材料与基板的粘接缺陷进行了无损检测。采用环氧树脂作为粘接剂,制作了隔热复合材料与基板的粘接样件。使太赫兹波聚焦在粘接层处,获得层析图像,根据焦平面附近的图像,可直观判断出粘接面有无缺陷。结合中值滤波,采用模糊c均值(FCM)聚类算法对缺陷图像进行分割得到含有目标缺陷信息的二值图像,计算像素点得到缺陷面积。通过与已知缺陷面积样件对比,对粘接缺陷面积的预测误差在±9%以内。     

基于模糊聚类的光电经纬仪多子弹弹道测量

本文对密集多子弹在空间坐标系中的左右相机弹点分布识别提出了一种新的方法。在各帧拍摄所得多子弹成像点中,提取子弹的形心坐标,根据子弹运动特点建立状态方程和测量方程,利用Kalman算法进行滤波。利用模糊c-均值聚类算法确定测量与弹丸轨迹的关联程度,从而实现弹道识别。仿真实验结果说明,该方法能有效地进行多子弹的弹道识别跟踪,并且算法简单、计算量小、易于工程实现。     

基于层次的K-means初始化算法

K-means算法是一种常用的聚类算法,但是聚类中心的初始化是其中的一个难点。笔者提出了一个基于层次思想的初始化方法。一般聚类问题均可看作加权聚类,通过层层抽样减少数据量,然后采用自顶向下的方式,从抽样结束层到原始数据层,每层都进行聚类,其中每层初始聚类中心均通过对上层聚类中心进行换算得到,重复该过程直到原始数据层,可得原始数据层的初始聚类中心。模拟数据和真实数据的实验结果均显示基于层次抽样初始化的K-means算法不仅收敛速度快、聚类质量高,而且对噪声不敏感,其性能明显优于现有的相关算法。     

Fuzzy ART/RRR-RSS: a two-phase neural network algorithm for part-machine grouping in cellular manufacturing

In this paper an efficient methodology adopting Fuzzy ART neural network is presented to solve the comprehensive part-machine grouping (PMG) problem in cellular manufacturing (CM). Our Fuzzy ART/RRR-RSS (Fuzzy ART/ReaRRangement-ReaSSignment) algorithm can effectively handle the real-world manufacturing factors such as the operation sequences with multiple visits to the same machine, production volumes of parts, and multiple copies of machines. Our approach is based on the non-binary production data-based part-machine incidence matrix (PMIM) where the operation sequences with multiple visits to the same machine, production volumes of parts, and multiple identical machines are incorporated simultaneously. A new measure to evaluate the goodness of the non-binary block diagonal solution is proposed and compared with conventional performance measures. The comparison result shows that our performance measure has more powerful discriminating capability than conventional ones. The Fuzzy ART/RRR-RSS algorithm adopts two phase approach to find the proper block diagonal solution in which all the parts and machines are assigned to their most preferred part families and machine cells for minimisation of inter-cell part moves and maximisation of within-cell machine utilisation. Phase 1 (clustering phase) attempts to find part families and machines cells quickly with Fuzzy ART neural network algorithm which is implemented with an ancillary procedure to enhance the block diagonal solution by rearranging the order of input presentation. Phase 2 (reassignment phase) seeks to find the best proper block diagonal solution by reassigning exceptional parts and machines and duplicating multiple identical machines to cells with the purpose of minimising inter-cell part moves and maximising within-cell machine utilisation. To show the robustness and recoverability of the Fuzzy ART/RRR-RSS algorithm to large-size data sets, a modified procedure of replicated clustering which starts with the near-best solution and rigorous qualifications on the number of cells and duplicated machines has been developed. Experimental results from the modified replicated clustering show that the proposed Fuzzy ART/RRR-RSS algorithm has robustness and recoverability to large-size ill-structured data sets by producing highly independent block diagonal solution close to the near-best one.     

A Development of Fuzzy Encoding and Decoding Through Fuzzy Clustering

Fuzzy clustering has emerged as a fundamental technique of information granulation. In this study, we introduce and discuss multivariable encoding and decoding mechanisms (referred altogether as a reconstruction problem) expressed in the language of fuzzy sets and fuzzy relations. The underlying performance index associated with the problem helps quantify a reconstruction error that arises when transforming a numeric datum through fuzzy sets (relations) and then reconstructing it into an original numeric format. The clustering platform considered in this study concerns the well-known algorithm of Fuzzy C-Means (FCM). The main design aspects deal with the relationships between the number of clusters versus the reconstruction properties and the resulting reconstruction error. The impact of the fuzzification coefficient on the reconstruction quality is investigated. This finding is of interest, given the fact that predominantly all applications involving FCM use the value of the fuzzification coefficient equal to 2. In light of the completed experiments, we demonstrate that this selection may not be experimentally legitimate. We also carry out a comparative analysis of the reconstruction properties of the Boolean decoding that is induced by the fuzzy partition. Experimental investigations involve selected machine learning data.