Fuzzy clustering with supervision

This study is concerned with clustering carried out in presence of labeled patterns. An objective of this optimization is to reconcile between the structure residing in data (and being primarily discovered by the underlying clustering mechanism) and the labels of the patterns forming such structure. In this sense, one can consider the supervised fuzzy clustering to be a framework of preliminary data analysis providing with a thorough insight into the structure of the data and supporting the ensuing design of detailed classifiers. The proposed method augments the standard fuzzy C-means algorithm by extending the original objective function by the supervision component (labeled patterns). Experimental results illustrate the approach and discuss the use of this type of clustering in vector quantization.     

一种遗传模糊聚类算法及其应用

研究一种基于遗传算法的模糊聚类方法,即将遗传算法得到的聚类中心作为模糊C-均值（FCM）聚类算法初值,这样既可以克服FCM算法对初始中心敏感的缺点,也可以解决遗传算法只能找到近似解的问题。将算法用于通信信号的星座聚类,根据聚类有效性函数自适应地确定聚类中心,并完成信号类型的识别。仿真实验证明,当存在较小的定时误差时,算法对PSK和QAM信号仍然是有效的。     

Fuzzy clustering algorithms for unsupervised change detection in remote sensing images

In this paper, we propose a context-sensitive technique for unsupervised change detection in multitemporal remote sensing images. The technique is based on fuzzy clustering approach and takes care of spatial correlation between neighboring pixels of the difference image produced by comparing two images acquired on the same geographical area at different times. Since the ranges of pixel values of the difference image belonging to the two clusters (changed and unchanged) generally have overlap, fuzzy clustering techniques seem to be an appropriate and realistic choice to identify them (as we already know from pattern recognition literatures that fuzzy set can handle this type of situation very well). Two fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson-Kessel clustering (GKC) algorithms have been used for this task in the proposed work. For clustering purpose various image features are extracted using the neighborhood information of pixels. Hybridization of FCM and GKC with two other optimization techniques, genetic algorithm (GA) and simulated annealing (SA), is made to further enhance the performance. To show the effectiveness of the proposed technique, experiments are conducted on two multispectral and multitemporal remote sensing images. A fuzzy cluster validity index (Xie-Beni) is used to quantitatively evaluate the performance. Results are compared with those of existing Markov random field (MRF) and neural network based algorithms and found to be superior. The proposed technique is less time consuming and unlike MRF does not require any a priori knowledge of distributions of changed and unchanged pixels.     

基于FCM和遗传算法的图像模糊聚类分析

聚类分析在模式识别和图像处理领域中有着极为重要的意义和广泛的应用前景。常用的聚类分析的方法是模糊C均值算法（FCM）,但是FCM算法容易陷入局部最优解。提出一种基于FCM和遗传算法对图像进行模糊聚类分析的方法。对输入图像进行纹理特征提取,通过主成分分析法对提取的特征向量进行降维处理,降低图像聚类分析算法的复杂度,提高结果的精确度,结合FCM和遗传算法对图像数据进行模糊聚类分析。实验结果表明该方法可以得到较好的分类效果。     

混沌粒子群与模糊聚类在图像分割中的应用

模糊C均值聚类算法是目前使用最广泛的模糊聚类算法,但是该算法也有其局限性,比如在迭代过程中对初始值非常敏感,极容易陷入局部极小值,以至于得不到最佳聚类结果。将粒子群优化算法应用到模糊C均值聚类算法中,提出一种基于混沌粒子群的模糊C均值聚类算法。它能够利用粒子群算法强大的全局寻优能力避免算法收敛于局部极值,最大程度上达到全局最佳聚类结果。为了避免粒子在迭代过程中停滞,该算法引入了混沌变量,以当前的全局最优位置来产生一个混沌序列,用混沌序列中拥有最优适应值的粒子随机代替当前粒子群中的一个粒子。将基于混沌粒子群的模糊C均值聚类算法应用于图像分割中,实验结果表明该算法能够有效地分割图像,并具有良好的鲁棒性和适应性。     

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.     

粒子群模糊聚类方法在病理图像分类中的应用

结合模糊C均值(FCM)算法局部搜索的特点,将PSO优化聚类结果作为后续FCM算法的初始值,使算法有很强的全局搜索能力.同时,采用Markov随机场与模糊聚类的耦合策略计算适应度函数,利用新的分类中心调整粒子位置,产生新的聚类中心,并将该方法应用于病理图像的分割.与传统的处理方法进行了比较,结果表明,该聚类更为准确且对病理图像的分割效果比原算法效果更好,但对于如何减少算法的运算量仍需作深入研究.     

一种基于遗传算法的聚类集成方法

聚类集成算法通常对聚类成员差异性要求较高,导致算法在生成聚类成员阶段计算复杂度提高。针对该问题提出了一种基于遗传算法的聚类集成方法CEGA,不考虑聚类成员的差异性,而是利用目标函数将聚类问题转化为聚类成员的优化问题,充分利用遗传算法内在的并行性和全局寻优能力,对聚类成员进行优化组合,并以得到的最优染色体作为聚类集成最终结果。分析了CEGA的复杂度及适用范围,并利用UCI数据库中部分数据集进行实验,实验结果表明这种聚类集成方法的有效性。     

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。     

Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining

Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and quantitative attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorithm, like CURE, to decide on fuzzy sets; for both cases the number of fuzzy sets is pre-specified. In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We achieve this by developing an automated clustering method based on multi-objective Genetic Algorithms (GA); the aim of the proposed approach is to automatically cluster values of a quantitative attribute in order to obtain large number of large itemsets in less time. We compare the proposed multi-objective GA based approach with two other approaches, namely: 1) CURE-based approach, which is known as one of the most efficient clustering algorithms; 2) Chien et al. clustering approach, which is an automatic interval partition method based on variation of density. Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al.’s work in terms of runtime, number of large itemsets and number of association rules.     

A survey of density based clustering algorithms

Density based clustering algorithms(DBCLAs)rely on the notion of density to identify clusters of arbitrary shapes,sizes with varying densities.Existing surveys on DB-CLAs cover only a selected set of algorithms.These surveys fail to provide an extensive information about a variety of DBCLAs proposed till date including a taxonomy of the algorithms.In this paper we present a comprehensive survey of various DB-CLAS over last two decades along with their classification.We group the DBCLAs in each of the four categories:density definition,parameter sensitivity,execution mode and nature of*data and further divide them into various classes under each of these categories.In addition,we compare the DBCLAs through their common features and variations in citation and conceptual dependencies.We identify various application areas of DBCLAS in domains such as astronomy,earth sciences,molecular biology,geography,multimedia.Our survey also identifies probable future directions of DBCLAs where involvement of density based methods may lead to favorable results.     

Hybrid adaptive predictive control for the multi-vehicle dynamic pick-up and delivery problem based on genetic algorithms and fuzzy clustering

In this paper, we develop a family of solution algorithms based upon computational intelligence for solving the dynamic multi-vehicle pick-up and delivery problem formulated under a hybrid predictive adaptive control scheme. The scheme considers future demand and prediction of expected waiting and travel times experienced by customers.     

小波变换编码在医学图像压缩中的应用

文章结合医学图像的特点探究了一种图像压缩编码方法：先对图像进行小波分解，然后针对不同层不同子图的特点对小波系数的各部分进行相应处理。小波分解后，低分辨率子图像的小波系数的动态变化范围大，因而采用BP神经网络进行自适应非线性预测编码，而对高分辨率子图像采用基于Kohonen网络的自组织特片映射（SOFM）算法的矢量量化进行编码，上述压缩方法可以在保证重构图像质量良好的情况下获得较大的压缩比，从而可以较好的满足医学图像存储的要求。     

Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data

Data mining is the process of discovering meaningful new correlation, patterns and trends by sifting through large amounts of data, using pattern recognition technologies as well as statistical and mathematical techniques. Cluster analysis is often used as one of the major data analysis technique widely applied for many practical applications in emerging areas of data mining. Two of the most delegated, partition based clustering algorithms namely k-Means and Fuzzy C-Means are analyzed in this research work. These algorithms are implemented by means of practical approach to analyze its performance, based on their computational time. The telecommunication data is the source data for this analysis. The connection oriented broad band data is used to find the performance of the chosen algorithms. The distance (Euclidian distance) between the server locations and their connections are rearranged after processing the data. The computational complexity (execution time) of each algorithm is analyzed and the results are compared with one another. By comparing the result of this practical approach, it was found that the results obtained are more accurate, easy to understand and above all the time taken to process the data was substantially high in Fuzzy C-Means algorithm than the k-Means.     

基于混沌粒子群和模糊聚类的图像分割算法*

模糊C-均值聚类算法(FCM)是一种结合模糊集合概念和无监督聚类的图像分割技术,适合灰度图像中存在着模糊和不确定的特点;但该算法受初始聚类中心和隶属度矩阵的影响,易陷入局部极小.利用混沌非线性动力学具有遍历性、随机性等特点,结合粒子群的寻优特性,提出了一种基于混沌粒子群模糊C-均值聚类(CPSO-FCM)的图像分割算法.实验证明,该方法不仅具有防止粒子因停顿而收敛到局部极值的能力,而且具有更快的收敛速度和更高的分割精度.     

Fuzzy transforms of monotone functions with application to image compression

This paper is focused on the special properties of functions (monotonicity, Lipschitz continuity) that are invariant with fuzzy transform. On the basis of the monotonicity invariance, we propose an efficient algorithm for improved image compression and reconstruction based on fuzzy transform.     

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。     

Extraction of major object features using VQ clustering for content-based image retrieval

An image representation method using vector quantization (VQ) on color and texture is proposed in this paper. The proposed method is also used to retrieve similar images from database systems. The basic idea is a transformation from the raw pixel data to a small set of image regions, which are coherent in color and texture space. A scheme is provided for object-based image retrieval. Features for image retrieval are the three color features (hue, saturation, and value) from the HSV color model and five textural features (ASM, contrast, correlation, variance, and entropy) from the gray-level co-occurrence matrices. Once the features are extracted from an image, eight-dimensional feature vectors represent each pixel in the image. The VQ algorithm is used to rapidly cluster those feature vectors into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to the object within the image. This method can retrieve similar images even in cases where objects are translated, scaled, and rotated.     

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