特征逐减的可能性模糊聚类算法

模糊C均值聚类（FCM）和可能性模糊C均值聚类（PFCM）没有考虑样本特征项及每个样本对聚类的贡献程度，存在对噪声较敏感的问题。特征减少的模糊聚类算法FRFCM可剔除数据集中无效特征量，且考虑了剩余特征量的权重，具有更好的聚类性能。对此，在可能性模糊C均值聚类算法（PFCM）的基础上将其与FRFCM算法相结合，提出新的特征逐减的可能性模糊C均值聚类算法（FRPFCM）。该算法解决了PFCM算法参数依赖的问题，且在迭代过程中可自动淘汰无效特征项并更新各特征项对聚类的贡献程度。对人工数据集以及UCI数据集进行测试的结果表明，提出的FRPFCM算法可得到更高的聚类准确率，所需迭代次数更少，算法收敛速度更快。     

区间型数据的模糊c均值聚类算法

提取区间型数据的特征值,给出适用于区间型数据模糊聚类的FCM算法族(IFCM)。该算法适用于不同特征样本数据的模糊聚类运算,并可对聚类结果进行优化。聚类效果的仿真比较表明,IFCM聚类的平均失真度比基于欧氏距离的FCM聚类算法低6.81%。由于距离定义的合理性,IFCM可以根据区间型数据的不同特点调整特征值的聚类权重,并推广至多维类型数据的模糊聚类。     

Possibilistic Exponential Fuzzy Clustering

Generally, abnormal points (noise and outliers) cause cluster analysis to produce low accuracy especially in fuzzy clustering. These data not only stay in clusters but also deviate the centroids from their true positions. Traditional fuzzy clustering like Fuzzy C-Means (FCM) always assigns data to all clusters which is not reasonable in some circumstances. By reformulating objective function in exponential equation, the algorithm aggressively selects data into the clusters. However noisy data and outliers cannot be properly handled by clustering process therefore they are forced to be included in a cluster because of a general probabilistic constraint that the sum of the membership degrees across all clusters is one. In order to improve this weakness, possibilistic approach relaxes this condition to improve membership assignment. Nevertheless, possibilistic clustering algorithms generally suffer from coincident clusters because their membership equations ignore the distance to other clusters. Although there are some possibilistic clustering approaches that do not generate coincident clusters, most of them require the right combination of multiple parameters for the algorithms to work. In this paper, we theoretically study Possibilistic Exponential Fuzzy Clustering (PXFCM) that integrates possibilistic approach with exponential fuzzy clustering. PXFCM has only one parameter and not only partitions the data but also filters noisy data or detects them as outliers. The comprehensive experiments show that PXFCM produces high accuracy in both clustering results and outlier detection without generating coincident problems.     

基于加权模糊c均值聚类的快速图像自动分割算法

图像分割是指将一幅图像分解为若干互不交迭的区域的集合，是图像处理和计算机视觉的基本问题之一。为了提高图像分割的效率，提出了一种基于2维直方图加权的塔形模糊c均值（FCM）聚类图像快速分割算法。该方法先通过构造合理的2维直方图对噪声进行抑制；然后通过塔形分解来缩减聚类样本集；最后利用加权FCM聚类算法进行分类。仿真结果表明，该方法的效率明显优于标准的FCM算法。此外，为确定分割的最优类别数c，还引入了一种基于该快速算法的聚类有效性评价函数——修正划分模糊度，实现了最佳图像分割类别数c的自动确定。基于人造图像和实际图像的测试实验结果表明该方法是有效的。     

一种Web用户访问路径的可能性模糊聚类算法

结合Web用户访问特点,针对Web用户访问路径聚类分析中普遍存在的对象类别不确定性现象进行了研究.结合模糊聚类和可能性聚类的特点,提出来一种新的用户访问路径的可能性模糊聚类算法.新方法通过定义相关的截集,自动地将对象分配到若干簇中,避免了人工干预,实现了交叉聚类的目的.新方法建立在leader聚类算法的框架上,只需要扫描数据集一遍使得算法效率大大提高.在标准数据集上的对比试验表明新算法不仅是有效的,而且效率较高.     

反一致可能性C均值聚类算法

可能性C均值聚类(PCM)因存在聚类中心一致性问题而导致PCM算法聚类失效.提出了反一致可能性C均值聚类算法(ACPCM),它将各聚类中心间距离的倒数之和构造为反一致性项,将反一致性项和PCM目标函数之和构成ACPCM目标函数,利用粒子群算法估计聚类中心,利用梯度法建立模糊隶属度迭代公式.理论分析和仿真实验验证了所提算法的聚类有效性及反一致性.     

基于阴影集的粗糙模糊可能性C均值聚类算法

相对于硬聚类算法,软聚类算法可以更好地表示具有不精确边界的类簇。粗糙集和模糊集均是用于描述不确定数据的有效的数学工具,二者互为补充。研究人员已经将粗糙集和模糊集的概念相结合,并应用到聚类算法中,提出了粗糙模糊可能性C均值聚类算法。而文中通过引入阴影集,有效地解决了粗糙模糊可能性C均值聚类算法中的阈值选择问题。     

Efficient Implementation of the Fuzzy c-Means Clustering Algorithms

This paper reports the results of a numerical comparison of two versions of the fuzzy c-means (FCM) clustering algorithms. In particular, we propose and exemplify an approximate fuzzy c-means (AFCM) implementation based upon replacing the necessary ``exact' variates in the FCM equation with integer-valued or real-valued estimates. This approximation enables AFCM to exploit a lookup table approach for computing Euclidean distances and for exponentiation. The net effect of the proposed implementation is that CPU time during each iteration is reduced to approximately one sixth of the time required for a literal implementation of the algorithm, while apparently preserving the overall quality of terminal clusters produced. The two implementations are tested numerically on a nine-band digital image, and a pseudocode subroutine is given for the convenience of applications-oriented readers. Our results suggest that AFCM may be used to accelerate FCM processing whenever the feature space is comprised of tuples having a finite number of integer-valued coordinates.     

Cluster Validity for the Fuzzy c-Means Clustering Algorithrm

The uniform data function is a function which assigns to the output of the fuzzy c-means (Fc-M) or fuzzy isodata algorithm a number which measures the quality or validity of the clustering produced by the algorithm. For the preselected number of cluster c, the Fc-M algorithm produces c vectors in the space in which the data lie, called cluster centers, which represent points about which the data are concentrated. It also produces for each data point c-membership values, numbers between zero and one which measure the similarity of the data points to each of the cluster centers. It is these membership values which indicate how the point is classified. They also indicate how well the point has been classified, in that values close to one indicate that the point is close to a particular center, but uniformly low memberships indicate that the point has not been classified clearly. The uniform data functional (UDF) combines the memberships in such a way as to indicate how well the data have been classified and is computed as follows. For each data point compute the ratio of its smallest membership to its largest and then compute the probability that one could obtain a smaller ratio (indicating better classification) from a clustering of a standard data set in which there is no cluster structure. These probabilities are then averaged over the data set to obtain the values of the UDF.     

基于Hadoop二阶段并行模糊c-Means数据聚类算法

为了解决MapReduce机制下算法通信时间占用比过高实际应用价值受限的问题,提出了基于Hadoop二阶段并行c-Means聚类算法;首先,采用成员管理协议方式实现成员管理与MapReduce降低操作的同步化方法,改进MapReduce机制下的MPI通讯管理方法;其次,实行典型个体组降低操作代替全局个体降低操作,并定义二阶段缓冲算法,通过第一阶段的缓冲进一步降低第二阶段MapReduce操作的数据量,尽可能降低大数据带来的对算法负面影响;通过仿真实验表明该算法在处理大数据上的性能表现较为优异;该算法在大规模数据集上的并行率和加速比都优于小型数据集上的表现,说明了该算法能够实时根据数据量的大小对自身进行调整。     

区间数的区间Ⅱ型模糊c均值聚类算法

针对区间数模糊c均值聚类算法存在模糊度指数m无法准确描述数据簇划分情况的问题,对点数据集合的区间Ⅱ型模糊c均值聚类算法进行拓展,将其扩展到区间型不确定数据的聚类中。同时,分析了区间数的区间Ⅱ型模糊c均值聚类算法的收敛性,以确定模糊度指数m1和m2的取值原则。基于合成数据和实测数据的仿真实验结果表明：区间数的区间Ⅱ型模糊c均值聚类算法比区间数的模糊c均值聚类算法的聚类效果好。     

基于改进的直觉模糊核聚类的图像分割方法

针对噪声图像模糊性的本质，提出了基于改进的直觉模糊核聚类的图像分割方法。采用直觉模糊集描述噪声图像包含的不确定性信息，将图像的灰度信息转换到直觉模糊域进行处理；将模糊核聚类拓展为直觉模糊核聚类，在图像的直觉模糊域进行聚类；通过高斯核函数和欧氏距离分别对像素8-邻域的灰度和空间信息进行建模，综合平衡灰度和空间信息对聚类的作用，并将其作为惩罚项加入到直觉模糊核聚类的目标函数中；通过梯度下降法，推导了迭代求解算法；通过典型的合成图像和自然图像分割实例，验证了所提算法的有效性和鲁棒性。     

一种基于马氏距离的可能性聚类方法

可能性聚类方法在数据分析和模式识别领域被广泛应用.本文通过选择马氏距离,构造一种特殊的基于马氏距离的可能性聚类方法.该方法在保持可能性聚类性能的同时,能有效地防止一致性采类的发生,依照最大最小概率原理,根据聚类的结果直接推断出聚类结果的误分下界,从而判断聚类的有效性.最后通过图像分割实验和标准数据集实验,验证了该算法的...     

基于指数可能性的新聚类算法

鉴于选择合理类别数对聚类的重要性,该文在指数可能性模型的基础上,通过一致函数,提出了基于指数可能性的新聚类方法,实验表明该方法能够在一定范围内确定合理类别数的同时实现有效的聚类,并且在图像分割中也能够获得合理的分割效果。     

一种基于PSO的模糊聚类算法

在大量的模糊聚类算法中,模糊C均值聚类算法是应用最为广泛的,然而它存在着一些缺点:对初始值敏感,对噪声数据敏感,容易陷入局部最优。针对以上问题,提出了一种基于粒子群优化的模糊聚类算法,利用粒子群强大的全局寻优能力,这种算法克服了模糊C均值聚类算法的缺点,试验证明,这种算法是一种很有潜力的模糊聚类算法。     

A Method of Face Recognition Based on Fuzzy c-Means Clustering and Associated Sub-NNs

The face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. In this paper, we present a method for face recognition based on parallel neural networks. Neural networks (NNs) have been widely used in various fields. However, the computing efficiency decreases rapidly if the scale of the NN increases. In this paper, a new method of face recognition based on fuzzy clustering and parallel NNs is proposed. The face patterns are divided into several small-scale neural networks based on fuzzy clustering and they are combined to obtain the recognition result. In particular, the proposed method achieved a 98.75% recognition accuracy for 240 patterns of 20 registrants and a 99.58% rejection rate for 240 patterns of 20 nonregistrants. Experimental results show that the performance of our new face-recognition method is better than those of the backpropagation NN (BPNN) system, the hard c-means (HCM) and parallel NNs system, and the pattern-matching system     

一种改进的可能性聚类算法及其有效性指标

可能性聚类有两大缺陷:一致聚类中心问题和有效性指标失效问题。对于第一个问题,有人提出在目标函数中添加聚类中心排斥项,但这样会引入更多的参数。为此,本文提出了一种改进的可能性聚类算法,较好地解决了这个问题。对于第二个问题,本文通过对隶属度作适当变换,使修正的有效性指标适用于可能性聚类。实验结果表明,该算法的优越性明显,有效性指标估计更为准确。     

基于PSO的可能性C均值聚类算法的研究

可能性C均值算法(PCM)是为了克服模糊C均值算法对噪声的敏感性而提出来的,但是它也存在一些缺陷,如易陷入局部最优,对初始条件敏感,导致聚类结果一致性等问题.针对以上问题,通过引进粒子群算法对其进行改进可以有效地避免这些问题,即提出了基于粒子群优化的可能性C均值聚类算法(PSO-PCM).基于粒子群优化的可能性C均值聚类方法首先对编码过的数据点进行优化,然后对该方法产生的中心点进行聚类,在聚类的过程中根据适应度函数再进行调节.通过对给定数据集的聚类测试,结果表明,基于粒子群优化的可能性C均值聚类方法在收敛速度和全局寻优能力等方面有较大的改进.     

一种区域型模糊聚类算法

使用支撑集和模糊待分集的概念来描述数据集的分布,在此基础上提出了一种区域型模糊聚类算法———re-gionalfuzzyclusteringalgorithm。仿真结果表明该算法有比常用的模糊C均值更好的聚类性能。     

基于模糊c均值聚类的RBFN的混炼胶粘度在线估计

介绍了将基于模糊c均值聚类(FCM)算法的多模型建模方法(简称FMM)与径向基函数神经网络(RBFM)相结合，先用FCM算法将训练集聚类，再用隶属度将子模型的输出结合起来，从而完成软测量模型的建立。这种方法不仅增强了在对象的整个输入空间的预测精度，同时减少了隐层节点数目，加快了学习速度。算法仿真表明，所提出的算法是处理橡胶混炼牯度软测量建模的一种很有效的方法。