基于多链量子蜂群算法的模糊聚类图像分割

针对传统模糊C-均值聚类算法对初始值和噪声敏感的缺点,提出了一种基于多链量子蜂群算法的模糊C-均值聚类算法。首先,将多链拓展编码方案应用到量子蜂群算法中,提出了多链量子蜂群算法;其次,利用多链量子蜂群算法来优化模糊C-均值聚类的初始聚类中心;最后,设计一种新的利用多链量子蜂群算法优化模糊C-均值聚类中心的图像分割算法。实验结果表明,所提出的基于多链量子蜂群算法的模糊C-均值聚类图像分割算法是有效的,相对于传统模糊C-均值聚类算法及基于模糊的人工蜂群算法,所提算法在分割正确率、分割速度及鲁棒性上均更有效。     

层流冷却过程中参数的模糊辨识方法

为了解决轧后温度控制中准确率和效率不足的问题,搭建了热轧带钢层流冷却动态模型,提出利用TS模糊模型对水冷换热系数进行建模.首先基于某热轧厂的工业实时数据,建立了层流冷却水冷换热系数的数据库.由于在TS模糊模型中,模糊C-均值聚类算法对初始聚类中心存在依赖性,使用人工蜂群模糊C-均值聚类算法计算隶属度函数和聚类中心,并使...     

基于核方法的并行模糊聚类算法

介绍并分析了模糊C-均值聚类算法、基于核方法的模糊C-均值聚类算法以及硬聚类算法.将硬聚类算法和模糊聚类算法结合起来,利用硬聚类算法初始化聚类中心,有效的减少模糊聚类算法的迭代次数.针对海量数据处理问题,将改进后的算法并行化,有效地提高了数据处理速度和效率,并在分布式互联PC环境下进行了性能测试.测试结果表明,基于核方法的并行模糊聚类算法具有很好的规模增长性和加速比.     

基于微粒群优化的模糊C-均值聚类图像分割

模糊C-均值聚类算法广泛用于图像分割,但存在聚类性能受类中心初始化影响,且计算量大等问题.为此,提出了一种基于微粒群的模糊C-均值聚类图像分割算法,该方法利用微粒群较强的搜索能力搜索聚类中心:由于搜索聚类中心是按密度进行,计算量小,故可以大幅提高模糊C-均值算法的计算速度.实验结果表明,该方法可以使模糊聚类的速度得到明显提高,实现图像的快速分割.     

一种C-均值聚类图像分割的模糊熵后处理方法

提出了一种结合C-均值聚类算法和模糊熵的图像分割方法，该方法先采用C均值聚类算法对含噪图像进行初步分割，再利用模糊熵准则作后续处理。该方法一方面能够继承C-均值聚类算法的优点，可以灵活地用在基于多特征和多阂值的图像分割中，另一方面充分考虑了图像的区域信息，利用模糊熵最小作为准则，对c均值聚类算法初步分割结果的错分类点作了进一步的处理，克服了C-均值聚类算法对噪声敏感的缺点。实验结果表明，本文方法在运算开销上只比C-均值聚类算法多4～6S，对于低信噪比的图像能够取得优于C-均值聚类算法的分割效果。     

基于粒子群优化算法的模糊聚类分析

提出一种基于模糊C-均值算法和粒子群优化算法的混合聚类算法,该算法利用粒子群优化算法全局寻优的特点,有效地克服了模糊C-均值算法对初始值敏感、易陷入局部最优的缺点.实验表明,该算法具备良好的聚类效果.     

基于连续域混合蚁群优化的核模糊C-均值聚类算法研究

为进一步提高核模糊C-均值聚类算法的聚类性能,提出基于连续域混合蚁群优化的核模糊C-均值聚类算法(KFCM-HACO),使用HACO对KFCM算法的内核函数参数值和聚类中心进行优化,克服传统算法弊端,使核模糊C-均值聚类算法的目标函数最小化,加快算法的收敛速度.该优化算法在UCI数据集上的仿真实验及结果比较表明,KFCM-HACO算法的聚类性能优于传统的聚类算法,提高了聚类的准确性.     

IFCM:改进的区间值数据的模糊C-均值聚类算法

对基于区间值数据的模糊聚类算法进行了研究,介绍了具有控制区间大小对聚类结果影响的加权因子的模糊C-均值聚类新算法.针对区间值数据模糊C-均值聚类新算法提出了一个适应距离的弹性系数,使算法得到改进,既能利用传统的FCM算法,又考虑了区间大小对聚类结果的影响,同时也能发现不规则的聚类子集,使聚类结果更加准确.     

基于改进的粒子群优化的模糊C-均值聚类算法

利用粒子群优化（PSO）算法全局寻优的特点,很大程度上避免了模糊C-均值聚类（FCM）算法对初值敏感、易陷入局部收敛的缺陷.利用收敛速度快的K均值聚类法得到的聚类中心作为PSO算法初始聚类中心的参考,提出一种新的模糊C-均值聚类算法Improved PSO FCM.实验结果表明,论文算法提高了FCM的搜索能力,聚类更为准确,效率更高.     

基于混合粒子群优化算法的聚类分析

针对模糊C-均值聚类算法易陷入局部最优和算法收敛速度慢等问题,提出了一种新的基于混合粒子群优化的模糊C-均值聚类算法.新算法在基本粒子群优化的模糊C-均值聚类算法的基础上结合了遗传算法的交叉、变异算子及混沌优化算法,并引入逃逸算子.仿真结果表明,该算法有效地避免了通常聚类方法易出现的早熟现象,同时也具有较快的收敛速度和较高的准确度.     

基于关联函数的动态聚类算法及应用

根据时序立体数据的特点,提出了基于关联函数一致性矩阵的动态聚类算法。给出了适用于时序立体数据关联函数的改进标准关联函数计算公式,并将该算法应用于乙烯裂解炉报警系统,结合流程的时序立体数据,得到了裂解炉报警系统的动态聚类分类结果,并验证了提出算法的有效性。本文算法对于时序数据的聚类具有较强的鲁棒性。     

基于基因表达式编程的多目标自动聚类算法

聚类是将物理或抽象对象的集合分成由类似的对象组成的多个类(簇)的过程.同一个簇中的对象彼此相似,而不同簇中的对象差异较大.以基因表达式编程算法为基础,结合新设计的广义聚类代数算子和目标优化函数,提出一种基于基因表达式编程的多目标自动聚类算法(MAGEP-Cluster).该算法不仅可以自动确定最优聚类的数目,还可以同时...     

Determining the number of clusters using information entropy for mixed data

In cluster analysis, one of the most challenging and difficult problems is the determination of the number of clusters in a data set, which is a basic input parameter for most clustering algorithms. To solve this problem, many algorithms have been proposed for either numerical or categorical data sets. However, these algorithms are not very effective for a mixed data set containing both numerical attributes and categorical attributes. To overcome this deficiency, a generalized mechanism is presented in this paper by integrating Rényi entropy and complement entropy together. The mechanism is able to uniformly characterize within-cluster entropy and between-cluster entropy and to identify the worst cluster in a mixed data set. In order to evaluate the clustering results for mixed data, an effective cluster validity index is also defined in this paper. Furthermore, by introducing a new dissimilarity measure into the k-prototypes algorithm, we develop an algorithm to determine the number of clusters in a mixed data set. The performance of the algorithm has been studied on several synthetic and real world data sets. The comparisons with other clustering algorithms show that the proposed algorithm is more effective in detecting the optimal number of clusters and generates better clustering results.     

一种有效的用于数据挖掘的动态概念聚类算法

概念聚类适用于领域知识不完整或领域知识缺乏时的数据挖掘任务.定义了一种基于语义的距离判定函数,结合领域知识对连续属性值进行概念化处理,对于用分类属性和数值属性混合描述数据对象的情况,提出了一种动态概念聚类算法DDCA(domain-baseddynamicclusteringalgorithm).该算法能够自动确定聚类数目,依据聚类内部属性值的频繁程度修正聚类中心,通过概念归纳处理,用概念合取表达式解释聚类输出.研究表明,基于语义距离判定函数和基于领域知识的动态概念聚类的算法DDCA是有效的.     

Visualization of fuzzy clusters by fuzzy Sammon mapping projection: application to the analysis of phase space trajectories

Since in practical data mining problems high-dimensional data are clustered, the resulting clusters are high-dimensional geometrical objects, which are difficult to analyze and interpret. Cluster validity measures try to solve this problem by providing a single numerical value. As a low dimensional graphical representation of the clusters could be much more informative than such a single value, this paper proposes a new tool for the visualization of fuzzy clustering results. By using the basic properties of fuzzy clustering algorithms, this new tool maps the cluster centers and the data such that the distances between the clusters and the data-points are preserved. During the iterative mapping process, the algorithm uses the membership values of the data and minimizes an objective function similar to the original clustering algorithm. Comparing to the original Sammon mapping not only reliable cluster shapes are obtained but the numerical complexity of the algorithm is also drastically reduced. The developed tool has been applied for visualization of reconstructed phase space trajectories of chaotic systems. The case study demonstrates that proposed FUZZSAMM algorithm is a useful tool in user-guided clustering.     

On mean shift-based clustering for circular data

Cluster analysis is a useful tool for data analysis. Clustering methods are used to partition a data set into clusters such that the data points in the same cluster are the most similar to each other and the data points in the different clusters are the most dissimilar. The mean shift was originally used as a kernel-type weighted mean procedure that had been proposed as a clustering algorithm. However, most mean shift-based clustering (MSBC) algorithms are used for numeric data. The circular data that are the directional data on the plane have been widely used in data analysis. In this paper, we propose a MSBC algorithm for circular data. Three types of mean shift implementation procedures with nonblurring, blurring and general methods are furthermore compared in which the blurring mean shift procedure is the best and recommended. The proposed MSBC for circular data is not necessary to give the number of cluster. It can automatically find a final cluster number with good clustering centers. Several numerical examples and comparisons with some existing clustering methods are used to demonstrate its effectiveness and superiority of the proposed method.     

基于MapReduce和改进密度峰值的划分聚类算法

对于基于划分的聚类算法随机选取初始聚类中心导致初始中心敏感,聚类结果不稳定、集群效率低等问题,提出一种基于MapReduce框架和改进的密度峰值的划分聚类算法(based on MapReduce framework and im-proved density peak partition clustering algorithm,MR-IDPACA).首先,通过自然最近邻定义新的局部密度计算方式,将搜索样本密度峰值点作为划分聚类算法的初始聚类中心;其次针对算法在大规模数据下运行时间复杂,提出基于E2LSH(exact Euclidean locality sensitive hashing)的一种分区方法,即KLSH(K of locality sensitive hashing).通过该方法对数据分区后结合MapReduce框架并行搜寻初始聚类中心,有效减少了算法在搜索初始聚类中心时的运行时间;对于MapReduce框架中的数据倾斜问题,提出ME(multistage equilibrium)策略对中间数据进行多段均衡分区,以提升算法运行效率;在MapReduce框架下并行聚类,得到最终聚类结果.实验得出MR-IDPACA算法在单机环境下有着较高的准确率和较强的稳定性,集群性能上也有着较好的加速比和运行时间,聚类效果有所提升.     

基于区间值数据动态聚类算法的客户市场细分

K均值算法(K-means)目前较为成功地应用于客户市场细分,但随着市场规模的扩大,面临着对于初始类个数敏感,易陷入局部极小值的严重问题,制约了聚类效果.提出基于区间值数据,以自适应欧氏距离作为度量的动态聚类方法,将客户的多维属性和基因算法结合提高类初始化质量,自适应地调整聚类数,并通过实验测试表现出较好的性能.     

Dynamic local search based immune automatic clustering algorithm and its applications

Based on clonal selection mechanism in immune system, a dynamic local search based immune automatic clustering algorithm (DLSIAC) is proposed to automatically evolve the number of clusters as well as a proper partition of datasets. The real based antibody encoding consists of the activation thresholds and the clustering centers. Then based on the special structures of chromosomes, a particular dynamic local search scheme is proposed to exploit the neighborhood of each antibody as much as possible so to realize automatic variation of the antibody length during evolution. The dynamic local search scheme includes four basic operations, namely, the external cluster swapping, the internal cluster swapping, the cluster addition and the cluster decrease. Moreover, a neighborhood structure based clonal mutation is adopted to further improve the performance of the algorithm. The proposed algorithm has been extensively compared with five state-of-the-art automatic clustering techniques over a suit of datasets. Experimental results indicate that the DLSIAC is superior to other five clustering algorithms on the optimum number of clusters found and the clustering accuracy. In addition, DLSIAC is applied to a real problem, namely image segmentation, with a good performance.     

一种量子行为粒子群优化动态聚类算法*

为了改善量子行为粒子群优化算法的收敛性能,避免粒子早熟问题,提出了一种基于完全学习策略的量子行为粒子群优化算法。由此设计了一种新的数据聚类算法,新的聚类算法通过特殊的粒子编码方式在聚类过程中能够自动确定最佳的聚类数目。在五个测试数据集上与其他两种动态聚类算法进行聚类实验比较,实验结果表明,基于完全学习策略的量子行为粒子群优化动态聚类算法能够获得较好的聚类结果,有着良好的应用前景。