Fuzzy clustering algorithms based on resolution and their application in image compression

This paper presents an idea of clustering resolution. On the basis of the idea, fuzzy clustering algorithms based on resolution are deduced, which naturally comprise a set of clustering algorithms. Thus, c-means algorithm and fuzzy c-means algorithms are actually special examples in the set. As an application for codebook design in image compression based on vector quantization, fuzzy clustering algorithms based on multiresolution are developed, which are almost prior to conventional algorithms in all aspects.     

Algorithms of fuzzy clustering with partial supervision

The paper is concerned with methods of fuzzy clustering based minimization of a scalar performance index with the aid of some labelled patterns. Some modifications of the performance index that take into account the results of partial supervision are proposed. The results of the algorithms introduced are exemplified by the use of numerical examples (Gustafson's cross and EKG data set).     

一种基于萤火虫算法的模糊聚类方法

针对模糊C-均值聚类对初始值敏感、容易陷入局部最优的缺陷,提出了一种基于萤火虫算法的模糊聚类方法。该方法结合萤火虫算法良好的全局寻优能力和模糊C-均值算法的较强的局部搜索特性,用萤火虫算法优化搜索FCM的聚类中心,利用FCM进行聚类,有效地克服了模糊C-均值聚类的不足,同时增强了萤火虫算法的局部搜索能力。实验结果表明,该算法具有很好的全局寻优能力和较快的收敛速度,能有效地收敛于全局最优解,具有较好的聚类效果。     

一种使用启发式搜索优化的模糊规则分类方法

文章提出了一种基于模糊规则的分类方法。该方法首先介绍了基于模糊C均值聚类的模糊规则提取,然后利用所建立的模糊规则库设计了一种分类算法,并且利用启发式搜索来精简分类规则。使用IRIS数据对该文的方法进行了性能测试,结果表明该方法在训练样本较少的情况下,能得到很好的分类效果,并且通过规则精简,所使用的规则数目大大下降,而分类性能更加优良。     

基于空间—色工多维向量模糊量化的彩色图象压缩

在Ｆｕｚｚｙｋ－ｍｅａｎｓ算法的基础上,考虑到真空蝇的色度信息与空间相关性,提出了空间一色度复合的多维向量模糊聚类量化方法它能有效地压缩彩色图象信息,量化失真小,并在不使图象边缘钝化的条件下去除图象的高频噪声。该方法在彩色图象在实验中取得了良好的效果。     

基于遗传算法的自适应文本模糊聚类研究

针对FCM聚类算法时初始聚类中心的选择敏感,以及聚类数C难以确定的问题,提出一种基于遗传算法的自适应文本模糊聚类方法.该方法首先将文档集合表示成向量空间模型,并采用一种新型的可变长染色体编码方案,随机选择文本向量作为初始聚类中心形成染色体,然后结合FCM算法的高效性和遗传算法的全局优化能力,通过遗传进化,有效地避免了局部最优解的出现,同时得到了优化的聚类数目和聚类结果.实验表明该算法是一种精确高效的文本聚类方法.     

基于空间-色度多维向量模糊量化的彩色图象压缩

在Ｆｕｚｚｙｋ－ｍｅａｎｓ算法的基础上,考虑到真实图象中的色度信息与空间相关性,提出了空间－色度复合的多维向量模糊聚类量化方法。它能有效地压缩彩色图象信息,量化失真小,并在不使图象边缘钝化的条件下去除图象的高频噪声。该方法在彩色图象处理的实验中取得了良好的效果     

Fast agglomerative clustering using information of k-nearest neighbors

In this paper, we develop a method to lower the computational complexity of pairwise nearest neighbor (PNN) algorithm. Our approach determines a set of candidate clusters being updated after each cluster merge. If the updating process is required for some of these clusters, k-nearest neighbors are found for them. The number of distance calculations for our method is O(N²), where N is the number of data points. To further reduce the computational complexity of the proposed algorithm, some available fast search approaches are used. Compared to available approaches, our proposed algorithm can reduce the computing time and number of distance calculations significantly. Compared to FPNN, our method can reduce the computing time by a factor of about 26.8 for the data set from a real image. Compared with PMLFPNN, our approach can reduce the computing time by a factor of about 3.8 for the same data set.     

An agglomerative clustering algorithm using a dynamic k-nearest-neighbor list

In this paper, a new algorithm is developed to reduce the computational complexity of Ward’s method. The proposed approach uses a dynamic k-nearest-neighbor list to avoid the determination of a cluster’s nearest neighbor at some steps of the cluster merge. Double linked algorithm (DLA) can significantly reduce the computing time of the fast pairwise nearest neighbor (FPNN) algorithm by obtaining an approximate solution of hierarchical agglomerative clustering. In this paper, we propose a method to resolve the problem of a non-optimal solution for DLA while keeping the corresponding advantage of low computational complexity. The computational complexity of the proposed method DKNNA + FS (dynamic k-nearest-neighbor algorithm with a fast search) in terms of the number of distance calculations is O(N²), where N is the number of data points. Compared to FPNN with a fast search (FPNN + FS), the proposed method using the same fast search algorithm (DKNNA + FS) can reduce the computing time by a factor of 1.90-2.18 for the data set from a real image. In comparison with FPNN + FS, DKNNA + FS can reduce the computing time by a factor of 1.92-2.02 using the data set generated from three images. Compared to DLA with a fast search (DLA + FS), DKNNA + FS can decrease the average mean square error by 1.26% for the same data set.     

A powerful hybrid clustering method based on modified stem cells and Fuzzy C-means algorithms

One of the simple techniques for Data Clustering is based on Fuzzy C-means (FCM) clustering which describes the belongingness of each data to a cluster by a fuzzy membership function instead of a crisp value. However, the results of fuzzy clustering depend highly on the initial state selection and there is also a high risk for getting the best results when the datasets are large. In this paper, we present a hybrid algorithm based on FCM and modified stem cells algorithms, we called it SC-FCM algorithm, for optimum clustering of a dataset into K clusters. The experimental results obtained by using the new algorithm on different well-known datasets compared with those obtained by K-means algorithm, FCM, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) Algorithm demonstrate the better performance of the new algorithm.     

蚁群优化与模糊聚类结合的文本聚类研究

针对模糊文本聚类算法（FCM）对输入顺序以及初始点敏感的问题,提出了一种使用蚁群优化的模糊聚类算法（FACA）。该算法采用蚁群聚类算法（ACA）找到聚类的初始中心点,以解决模糊聚类的输入顺序以及初始点敏感等问题。模糊文本聚类算法的线性复杂度使其更便于在计算机实现。与经典的基本模糊聚类以及蚁群聚类在真实数据集上仿真相比较,结果表明经蚁群优化过的模糊聚类算法（FACA）效果更有效,更适合应用于大型的数据集。     

基于模糊聚类的图书目录查询方法研究

在此提出一种基于模糊聚类的目录查询新方法,该方法基于模糊C均值聚类算法,并结合了编辑距离算法。针对传统的模糊C均值聚类算法的聚类结果不稳定性问题,引入了高权样本点集;并且在处理聚类过程中的边界值归属不足问题,引入编辑距离算法。     

基于密度峰值优化的模糊C均值聚类算法

针对传统模糊C均值聚类算法和基于K-means++优化聚类中心的模糊C均值算法存在初始聚类中心敏感、聚类速度收敛慢、聚类算法需要人为给定聚类数目等缺陷,受密度峰值聚类算法（Clustering by Fast Search and Find of Density Peaks,CFSFDP）的启发,提出了基于密度峰值算法优化的模糊C均值聚类算法,自适应产生初始聚类中心,确定聚类数目,并优化算法收敛过程。实验结果表明,改进后的算法与传统模糊聚类C均值算法相比能够准确地得到簇的数目,性能有明显的提高,并加快算法的收敛速度,达到相对更好的聚类效果。     

A tabu search-based algorithm for the fuzzy clustering problem

The Fuzzy Clustering Problem (FCP) is a mathematical program which is difficult to solve since it is nonconvex, which implies possession of many local minima. The fuzzy C-means heuristic is the widely known approach to this problem, but it is guaranteed only to yield local minima. In this paper, we propose a new approach to this problem which is based on tabu search technique, and aims at finding a global solution of FCP. We compare the performance of the algorithm with the fuzzy C-means algorithm.     

一种基于Tabu搜索的模糊聚类方法

首先,对模糊C 均值聚类算法做了简要分析和评论,在此基础上,将Tabu搜索引入模糊聚类,以克服模糊C 均值聚类的局部性和对初始聚类中心的敏感性,采用了适合于模糊聚类的树型编码方案。然后,给出了新算法的实现方法及步骤。仿真实验表明,新方法在速度和解的质量方面都达到了令人满意的效果。     

模糊C-均值算法在直觉模糊数聚类中的应用

提出了直觉模糊数的非监督模糊C-均值聚类算法。该算法首先定义了直觉模糊数之间的距离,其次构造了直觉模糊数聚类问题的目标函数,最后得到了直觉模糊数聚类的模糊C-均值聚类算法,聚类中心初始化方法,以及相关的聚类有效性函数。实验结果表明,该算法是有效的。     

基于属性权重区间监督的模糊C均值聚类算法

在加权模糊聚类算法中,属性权重确定的合理性是一个重要问题.鉴于用区间数描述决策者推理模糊性的优越性,提出属性权重用区间数表示,由区间层次分析法获得属性对聚类的贡献度,并以该区间为约束条件,提出了可同时获得属性权重和聚类结果的模糊C均值聚类新算法.实验结果表明,该算法以决策者的经验和偏好为监督,可避免迭代计算陷入不必要的局部极小解,能够提高权重分配的合理性,进而得到了更为准确的聚类结果.     

Data reconstruction with information granules: An augmented method of fuzzy clustering

Information granules form an abstract and efficient characterization of large volumes of numeric data. Fuzzy clustering is a commonly encountered information granulation approach. A reconstruction (degranulation) is about decoding information granules into numeric data. In this study, to enhance quality of reconstruction, we augment the generic data reconstruction approach by introducing a transformation mapping of the originally produced partition matrix and setting up an adjustment mechanism modifying a localization of the prototypes. We engage several population-based search algorithms to optimize interaction matrices and prototypes. A series of experimental results dealing with both synthetic and publicly available data sets are reported to show the enhancement of the data reconstruction performance provided by the proposed method.     

基于自适应权重的模糊C-均值聚类算法

针对模糊C-均值聚类算法过度依赖初始聚类中心的选取,从而易受孤立点和样本分布不均衡的影响而陷入局部最优状态的不足,提出一种基于自适应权重的模糊C-均值聚类算法。该算法采用高斯距离比例表示权重,在每一次迭代过程中,根据当前数据的聚类划分情况,动态计算每个样本对于类的权重,降低了算法对初始聚类中心的依赖,减弱了孤立点和样本分布不均衡的影响。实验结果表明,该算法是一种较优的聚类算法,具有更好的健壮性和聚类效果。     

模糊C-均值聚类新算法在说话人辨认中的应用

该文提出了一种将模糊C-均值聚类法的各种改进算法与矢量量化法相结合的说话人辨认的新方法。首先从语音信号中提取MFCC特征矢量,其次利用矢量量化来设计码书,最后用改进算法对待识语音进行辨认。新算法的辨认率达到95%以上,抗噪性能也优于矢量量化法。