基于密度函数加权的模糊C均值聚类算法研究

模糊聚类算法具有较强的实用性,但传统模糊C均值算法（FCM）具有对样本集进行等划分趋势的缺陷,没有考虑不同样本的实际分布对聚类效果的影响,当数据集中各样本密集程度相差较大时,聚类结果不是很理想。因此,提出一种基于密度函数加权的模糊C均值聚类算法（DFCM算法）,该算法利用数据对象的密度函数作为每个数据点权值。实验结果表明,与传统的模糊C均值算法相比,DFCM算法具有较好的聚类效果。     

基于两种加权方式的模糊聚类算法*

模糊聚类是一种应用广泛的数据分析和建模的无监督方法,但该算法受离群点影响较大,并且没有考虑样本数据中各维特征对聚类贡献程度的不同.针对这两个问题,提出了基于两种加权方式的聚类算法,该算法定义了一种新的样本加权的概念,减弱了离群点对聚类的干扰,同时为数据样本的每一维特征赋予一个权值,使聚类更加准确.仿真实验结果验证了该算...     

基于K近邻加权的混合C均值聚类算法

该文提出了一种基于K近邻加权的混合C均值聚类算法。首先该文利用模糊C均值聚类和可能性C均值聚类的优点,设计出一种混合C均值聚类算法。然后以K近邻规则为基础,计算出样本集的加权矩阵,最后得到基于K近邻加权的混合C均值聚类算法。由于该算法考虑到了不同样本点对分类的影响程度,对较复杂的样本集合,能明显提高分类的正确性和鲁棒性。     

基于鲁棒加权模糊聚类的污水处理过程监测方法

针对非线性强、先验故障知识少、异常工况识别难的污水处理过程监测问题,提出一种基于鲁棒加权模糊c均值(Robust weighted fuzzy c-means, RoW-FCM)聚类与核偏最小二乘(Kernel partial least squares, KPLS)的过程监测方法.首先,针对污水处理过程的高维非线性耦合特性,采用核偏最小二乘对高维输入变量进行降维;其次,针对传统基于最近邻分配的模糊c均值算法对离群点敏感以及存在聚类不平衡簇的问题,提出充分考虑样本间相互关系的基于鲁棒加权模糊c均值聚类算法.通过引入可能性划分矩阵作为权值参数实现不同样本数据的区分加权,提高了离群点数据聚类的鲁棒性,同时引入聚类大小控制参数解决不平衡簇的问题.进一步将基于鲁棒加权模糊c均值算法对核偏最小二乘降维后的得分矩阵进行聚类,利用聚类得到的隶属度矩阵实现异常工况的检测;最后,建立隶属度矩阵与过程变量的回归模型,并利用得到的变量贡献矩阵描述变量对各个簇的解释程度,实现异常工况的识别.数值仿真以及污水处理过程数据实验表明该方法具有更好的鲁棒性能,在异常工况检测和识别上具有较好的效果.     

一种加权模糊C均值聚类算法及其在图像分割中的应用

现有的加权模糊C均值聚类算法中,属性加权是一个不断迭代、重复计算的过程,费时费力。针对这种情况,提出Fisher线性判别率进行属性加权。算法首先直接计算每一维属性对模糊聚类的贡献度,其次对所有属性的贡献度进行归一化处理然后加权聚类。在人工和实际数据集所做实验表明:该算法在提高聚类速度的同时,聚类效果上也优于其他同类加权模糊C均值聚类算法。     

单点逼近型加权模糊C均值算法的遥感图像聚类应用

针对模糊C均值算法对数据分布状态和初始聚类中心过于依赖的问题,利用已知样本信息,提出了一种改进的单点逼近型加权模糊C均值算法.该算法首先通过对原始数据进行概率统计和加入样本属性权值来调整数据为均匀分布;然后采用先验样本单点逼近的方法来消除先验样本选取的影响,从而不仅得到了合适的初始聚类中心,而且有效地加快了算法的收敛速度和提高了聚类的精度;最后将改进后算法与遥感数据特点相结合,构成了完整的遥感图像地物聚类算法.通过UCI数据集和扎龙湿地遥感数据的试验结果的比较证明,该改进方法是真实有效的.     

半监督加权模糊C均值聚类算法

对于团状、每类样本数相差较大的数据集,FCM算法和半监督模糊C均值聚类算法都不是最佳聚类方法,因为它们对数据集有等划分趋势。针对这种情况,利用样本点分布密度大小作为权值,结合半监督学习方法,提出半监督点密度加权模糊C均值聚类算法。在半监督学习过程中,对于求极值的问题采用模拟退火算法。结果证明,点密度加权模糊C均值聚类算法确实能提高聚类精度。     

一种改进的模糊C均值聚类算法

针对数据集中若存在孤立点或者是噪声数据会影响模糊C均值聚类算法(FCM)的聚类性能问题,本文将离群点的辨认方法与FCM算法相结合,提出一种改进的FCM聚类算法。该算法有效地降低了孤立点或噪声数据对正常数据的影响,提高了FCM算法的聚类精度。将该算法在入侵检测系统中进行实验验证,通过与FCM算法进行对比分析,证明了该算法的有效性和可行性。     

离群模糊核聚类算法

一般说来,离群点是远离其他数据点的数据,但很可能包含着极其重要的信息.提出了一种新的离群模糊核聚类算法来发现样本集中的离群点.通过Mercer核把原来的数据空间映射到特征空间,并为特征空间的每个向量分配一个动态权值,在经典的FCM模糊聚类算法的基础上得到了一个特征空间内的全新的聚类目标函数,通过对目标函数的优化,最终得到了各个数据的权值,根据权值的大小标识出样本集中的离群点.仿真实验的结果表明了该离群模糊核聚类算法的可行性和有效性.     

基于离群点剔除的网络热点事件挖掘算法

研究网络热点事件准确检测问题,收集网络文本数据中含有大量的离群点,由于离群点一些噪声数据,对网络热点事件检测产生不利影响.为提高检测正确率,提出一种采用离群点剔除的网络热点事件挖掘算法.首先通过计算数据点的密度相似度,将小于阈值的离群点剔除,降低计算复杂度和离群点的不利影响,然后采用模糊C均值聚类算法对网络文本进行聚类,发现其中的热点事件,最后通过仿真测试算法的有效性.仿真结果表明,改进算法剔除网络中的离群点,不仅提高了网络热点事件检测正确率,而且降低算法计算复杂度,加快了网络热点事件挖掘速度,更加适合于网络热点事件在线挖掘要求.     

Generalized weighted conditional fuzzy clustering

Fuzzy clustering helps to find natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. Among many existing modifications of this method, conditional or context-dependent c-means is the most interesting one. In this method, data vectors are clustered under conditions based on linguistic terms represented by fuzzy sets. This paper introduces a family of generalized weighted conditional fuzzy c-means clustering algorithms. This family include both the well-known fuzzy c-means method and the conditional fuzzy c-means method. Performance of the new clustering algorithm is experimentally compared with fuzzy c-means using synthetic data with outliers and the Box-Jenkins database.     

Extended Gaussian kernel version of fuzzy c-means in the problem of data analyzing

Fuzzy c-means clustering with spatial constraints is considered as suitable algorithm for data clustering or data analyzing. But FCM has still lacks enough robustness to employ with noise data, because of its Euclidean distance measure objective function for finding the relationship between the objects. It can only be effective in clustering ‘spherical’ clusters, and it may not give reasonable clustering results for “non-compactly filled” spherical data such as “annular-shaped” data. This paper realized the drawbacks of the general fuzzy c-mean algorithm and it tries to introduce an extended Gaussian version of fuzzy C-means by replacing the Euclidean distance in the original object function of FCM. Firstly, this paper proposes initial kernel version of fuzzy c-means to aim at simplifying its computation and then extended it to extended Gaussian kernel version of fuzzy c-means. It derives an effective method to construct the membership matrix for objects, and it derives a robust method for updating centers from extended Gaussian version of fuzzy C-means. Furthermore, this paper proposes a new prototypes learning method and it obtains initial cluster centers using new mathematical initialization centers for the new effective objective function of fuzzy c-means, so that this paper tries to minimize the iteration of algorithms to obtain more accurate result. Initial experiment will be done with an artificially generated data to show how effectively the new proposed Gaussian version of fuzzy C-means works in obtaining clusters, and then the proposed methods can be implemented to cluster the Wisconsin breast cancer database into two clusters for the classes benign and malignant. To show the effective performance of proposed fuzzy c-means with new initialization of centers of clusters, this work compares the results with results of recent fuzzy c-means algorithm; in addition, it uses Silhouette method to validate the obtained clusters from breast cancer datasets.     

基于模糊c-means与自适应粒子群优化的模糊聚类算法

已有的粒子群模糊聚类算法需要设置粒子群参数并且收敛速度较慢,对此提出一种基于改进粒子群与模糊c-means的模糊聚类算法。首先,使用模糊c-means算法生成一组起始解,提高粒子群演化的方向性;然后,使用改进的自适应粒子群优化方法对数据进行训练与优化,训练过程中自适应地调节粒子群参数;最终,采用模糊c-means算法进行模糊聚类过程。对比实验结果表明,所提方法大幅度提高了计算速度,并获得了较高的聚类性能。     

FCM融合改进的GSA算法在医学图像分割中的研究

医学图像由于具有复杂性,在对其进行图像分割时存在很大的不确定性,为了提高模糊c均值聚类算法(FCM)在处理医学图像分割时的性能,提出一种新的混合方法进行图像分割。利用FCM算法将图像像素分成均匀的区域,融合引力搜索算法,将改进的引力搜索算法纳入模糊c均值聚类算法中,以找到最优聚类中心,使模糊c均值聚类的适应度函数值最小,从而提高分割效果。实验结果表明,相对于传统的聚类算法,所提算法在分割复杂的医学图像方面更具有效性。     

基于ELM的局部空间信息的模糊C均值聚类图像分割算法

极限学习机（Extreme learning machine, ELM）作为一种新技术具有在回归和分类中良好的泛化性能。局部空间信息的模糊C均值算法(Weighted fuzzy local information C-means, WFLICM)用邻域像素点的空间信息标记中心点的影响因子,增强了模糊C均值聚类算法的去噪声能力。基于极限学习机理论,对WFLICM进行改进优化,提出了基于ELM的局部空间信息的模糊C均值聚类图像分割算法(New kernel weighted fuzzy local information C-means based on ELM,ELM-NKWFLICM)。该方法基于ELM特征映射技术,将原始数据通过ELM特征映射技术映射到高维ELM隐空间中,再用改进的新核局部空间信息的模糊C均值聚类图像分割算法(New kernel weighted fuzzy local information C-means,NKWFLICM)进行聚类。 实验结果表明 ELM-NKWFLICM算法具有比WFLICM算法更强的去噪声能力,且很好地保留了原图像的细节,算法在处理复杂非线性数据时更高效, 同时克服了模糊聚类算法对模糊指数的敏感性问题。     

DCT子空间的邻域加权模糊C均值聚类算法

模糊C均值聚类是一种有效的图像分割方法, 但存在因忽略空间上下文信息和结构信息而易为噪声所干扰的现象. 为此提出了DCT子空间的邻域加权模糊C均值聚类方法. 该方法首先结合分块的思想, 对图像块进行离散余弦变换(discrete cosine transform,DCT), 建立了一个基于图像块局部信息的相似性度量模型; 然后定义目标函数中的欧式距离为邻域加权距离; 最后将该方法应用于加噪的人工合成图像、自然图像和MR图像. 实验结果表明, 该方法能够获得较好的分割效果, 同时具有较强的抗噪性.     

一种模糊规则提取方法的研究

提出了一种基于改进的模糊 C 均值聚类的模糊规则提取方法。然后基于所提取的模糊规则给出了一种分类算法,并利用 IRIS 数据对此分类算法进行了仿真测试。结果表明,该算法在训练祥本较少的情况下,仍能得到很好的分类效果,由此说明所提出的模糊规则生成方法有效。     

Fuzzy c-means improvement using relaxed constraints support vector machines

Fuzzy clustering is a widely applied method for extracting the underlying models within data. It has been applied successfully in many real-world applications. Fuzzy c-means is one of the most popular fuzzy clustering methods because it produces reasonable results and its implementation is straightforward. One problem with all fuzzy clustering algorithms such as fuzzy c-means is that some data points which are assigned to some clusters have low membership values. It is possible that many samples may be assigned to a cluster with low-confidence. In this paper, an efficient and noise-aware implementation of support vector machines, namely relaxed constraints support vector machines, is used to solve the mentioned problem and improve the performance of fuzzy c-means algorithm. First, fuzzy c-means partitions data into appropriate clusters. Then, the samples with high membership values in each cluster are selected for training a multi-class relaxed constraints support vector machine classifier. Finally, the class labels of the remaining data points are predicted by the latter classifier. The performance of the proposed clustering method is evaluated by quantitative measures such as cluster entropy and Minkowski scores. Experimental results on real-life data sets show the superiority of the proposed method.     

一种基于Mahalanobis距离的增量聚类算法

经典的模糊c均值聚类算法对非球型或椭球型分布的数据集进行聚类效果较差。将经典的模糊c均值聚类中的欧氏距离用Mahalanobis距离替代,利用Mahalanobis距离的优点,将其用于增量学习中,提出一种基于马氏距离的模糊增量聚类学习算法。实验结果表明该算法能较有效地解决模糊聚类方法中的缺陷,提高了训练精度。     

Effective fuzzy c-means clustering algorithms for data clustering problems

Clustering is a well known technique in identifying intrinsic structures and find out useful information from large amount of data. One of the most extensively used clustering techniques is the fuzzy c-means algorithm. However, computational task becomes a problem in standard objective function of fuzzy c-means due to large amount of data, measurement uncertainty in data objects. Further, the fuzzy c-means suffer to set the optimal parameters for the clustering method. Hence the goal of this paper is to produce an alternative generalization of FCM clustering techniques in order to deal with the more complicated data; called quadratic entropy based fuzzy c-means. This paper is dealing with the effective quadratic entropy fuzzy c-means using the combination of regularization function, quadratic terms, mean distance functions, and kernel distance functions. It gives a complete framework of quadratic entropy approaching for constructing effective quadratic entropy based fuzzy clustering algorithms. This paper establishes an effective way of estimating memberships and updating centers by minimizing the proposed objective functions. In order to reduce the number iterations of proposed techniques this article proposes a new algorithm to initialize the cluster centers.In order to obtain the cluster validity and choosing the number of clusters in using proposed techniques, we use silhouette method. First time, this paper segments the synthetic control chart time series directly using our proposed methods for examining the performance of methods and it shows that the proposed clustering techniques have advantages over the existing standard FCM and very recent ClusterM-k-NN in segmenting synthetic control chart time series.