Modified semi-supervised affinity propagation clustering with fuzzy density fruit fly optimization

Neural Computing and Applications - Affinity propagation (AP) is a clustering method that takes as input measures of similarity between pairs of data points. As the oscillations and preference...     

基于仿射传播聚类和高斯过程的多模型建模方法

针对单模型建模存在泛化能力差的问题,提出一种基于仿射传播聚类和高斯过程的多模型建模方法。该方法定义了一种新的相似度使仿射传播聚类算法把样本数据按照不同的工作点进行聚类,获得的子聚类样本数据再分别使用高斯过程建立相应的子模型,用"切换开关"方式组合作为最终模型的输出。将该建模方法应用到某双酚A反应釜出口丙酮含量的软测量建模中,仿真结果表明该方法具有较高的估计精度和一定的实用价值。     

基于仿射传播聚类和动态时间弯曲距离的LS-SVM在线建模方法

针对多批次多工况化工过程,离线模型易老化失效和不易满足工业生产的实时优化控制问题,提出一种基于仿射传播聚类和动态时间弯曲距离的LS-SVM在线建模方法。该方法首先利用仿射传播聚类算法对各批次样本进行工况划分,再考虑样本间的时间有序性,由包含待测样本的一段时间序列作为查询序列,并以动态时间弯曲距离来衡量序列间的相似情况,从各历史批次相应的工况阶段获取相似样本片段,构建训练样本集,最后采用最小二乘支持向量机建立在线预测模型。将该方法用于青霉素浓度预测中,仿真研究表明,所提方法提高了建模预测精度和泛化能力。     

Pair-copula based mixture models and their application in clustering

Finite mixtures are often used to perform model based clustering of multivariate data sets. In real life applications, such data may exhibit complex nonlinear form of dependence among the variables. Also, the individual variables (margins) may follow different families of distributions. Most of the existing mixture models are unable to accommodate these two aspects of the data. This paper presents a finite mixture model that involves a pair-copula based construction of a multivariate distribution. Such a model de-couples the margins and the dependence structures. Hence, the margins can be modeled using different families. Again, many possible dependence structures can also be studied using different copulas. The resulting mixture model (called DVMM) is then capable of capturing a broad family of distributions including non-Gaussian models. Here we study DVMM in the context of clustering of multivariate data. We design an expectation maximization procedure for estimating the mixture parameters. We perform extensive experiments on the basis of a number of well-known data sets. A detailed evaluation of the clustering quality obtained by DVMM in comparison to other mixture models is presented. The experimental results show that the performance of DVMM is quite satisfactory.     

Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm

A novel method based on rough sets (RS) and the affinity propagation (AP) clustering algorithm is developed to optimize a radial basis function neural network (RBFNN). First, attribute reduction (AR) based on RS theory, as a preprocessor of RBFNN, is presented to eliminate noise and redundant attributes of datasets while determining the number of neurons in the input layer of RBFNN. Second, an AP clustering algorithm is proposed to search for the centers and their widths without a priori knowledge about the number of clusters. These parameters are transferred to the RBF units of RBFNN as the centers and widths of the RBF function. Then the weights connecting the hidden layer and output layer are evaluated and adjusted using the least square method (LSM) according to the output of the RBF units and desired output. Experimental results show that the proposed method has a more powerful generalization capability than conventional methods for an RBFNN.     

Fuzzy order statistics and their application to fuzzy clustering

The median and the median absolute deviation (MAD) are robust statistics based on order statistics. Order statistics are extended to fuzzy sets to define a fuzzy median and a fuzzy MAD. The fuzzy c-means (FCM) clustering algorithm is defined for any p-norm (pFCM), including the l₁-norm (1FCM), The 1FCM clustering algorithm is implemented via the alternating optimization (AO) method and the clustering centers are shown to be the fuzzy median. The resulting AO-1FCM clustering algorithm is called the fuzzy c-medians (FCMED) clustering algorithm. An example illustrates the robustness of the FCMED     

Novel method for detection of mixed-type defect patterns in wafer maps based on a single shot detector algorithm

Journal of Intelligent Manufacturing - In semiconductor manufacturing, detecting defect patterns is important because they are directly related to the root causes of failures in the wafer process....     

An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation

The Journal of Supercomputing - Spectral clustering algorithm has become more popular in data clustering problems in recent years, due to the idea of optimally dividing the graph to solve the data...     

Fuzzy optimized self-organizing maps and their application to document clustering

In this paper, an approach using fuzzy logic techniques and self-organizing maps (SOM) is presented in order to manage conceptual aspects in document clusters and to reduce the training time. In order to measure the presence degree of a concept in a document, a concept frequency formula is introduced. This formula is based on new fuzzy formulas to calculate the polysemy degree of terms and the synonymy degree between terms. In this approach, new fuzzy improvements such as automatic choice of the topology, heuristic map initialization, a fuzzy similarity measure and a keywords extraction process are used. Some experiments have been carried out in order to compare the proposed system with classic SOM approaches by means of Reuters collection. The system performance has been measured in terms of F-measure and training time. The experimental results show that the proposed approach generates good results with less training time compared to classic SOM techniques.     

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.     

Linear fuzzy clustering techniques with missing values and their application to local principal component analysis

In this paper, we propose two methods for partitioning an incomplete data set with missing values into several linear fuzzy clusters by extracting local principal components. One is an extension of fuzzy c-varieties clustering that can be regarded as the algorithm for the local principal component analysis of fuzzy covariance matrices. The other is a simultaneous application of fuzzy clustering and principal component analysis of fuzzy correlation matrices. Both methods estimate prototypes ignoring only missing values and they need no preprocessing of data such as the elimination of samples with missing values or the imputation of missing elements. Numerical examples show that the methods provide useful tools for interpretation of the local structures of a database.     

Correction to: An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation

The Journal of Supercomputing -     

结合图模型的优化多类SVM及智能交通应用

为提高多类支持向量机分类器对多目标的分类准确度,提出一种结合无向图模型优化的多类支持向量机分类器。首先,利用余弦测度计算训练数据之间的相似度,构建包含训练数据和相似度矩阵的无向图模型,求解相似度约束矩阵。然后,将相似度约束矩阵引入多类支持向量机求解的目标函数,构建优化的多类支持向量机分类器。最后,将优化的多类支持向量机分类器用于智能交通领域,结合梯度方向直方图特征检测行人和车辆目标。实验表明,该方法检测行人和车辆目标的错误率低于经典的多类支持向量机分类器和目前主流的目标检测方法。     

The single vehicle routing problem with deliveries and selective pickups

The single vehicle routing problem with deliveries and selective pickups (SVRPDSP) is defined on a graph in which pickup and delivery demands are associated with customer vertices. The difference between this problem and the single vehicle routing problem with pickups and deliveries (SVRPPD) lies in the fact that it is no longer necessary to satisfy all pickup demands. In the SVRPDSP a pickup revenue is associated with each vertex, and the pickup demand at that vertex will be collected only if it is profitable to do so. The net cost of a route is equal to the sum of routing costs, minus the total collected revenue. The aim is to design a vehicle route of minimum net cost, visiting each customer, performing all deliveries, and a subset of the pickups. A mixed integer linear programming formulation is proposed for the SVRPDSP. Classical construction and improvement heuristics, as well as a tabu search heuristic (TS), are developed and tested on a number of instances derived from VRPLIB. Computational results show that the solutions produced by the proposed heuristics are near-optimal. There is also some evidence that the best solutions identified by the heuristics are frequently non-Hamiltonian and may contain one or two customers visited twice.     

Multiple criteria mathematical programming for multi-class classification and application in network intrusion detection

Multi-class classification problems are harder to solve and less studied than binary classification problems. The goal of this paper is to present a multi-criteria mathematical programming (MCMP) model for multi-class classification. Furthermore, we introduce the concept of e-support vector to facilitate computation of large-scale applications. Instead of finding the optimal solution for a convex mathematical programming problem, the computation of optimal solution for the model requires only matrix computation. Using two network intrusion datasets, we demonstrate that the proposed model can achieve both high classification accuracies and low false alarm rates for multi-class network intrusion classification.     

Cooperative and penalized competitive learning with application to kernel-based clustering

Competitive learning approaches with individual penalization or cooperation mechanisms have the attractive ability of automatic cluster number selection in unsupervised data clustering. In this paper, we further study these two mechanisms and propose a novel learning algorithm called Cooperative and Penalized Competitive Learning (CPCL), which implements the cooperation and penalization mechanisms simultaneously in a single competitive learning process. The integration of these two different kinds of competition mechanisms enables the CPCL to locate the cluster centers more quickly and be insensitive to the number of seed points and their initial positions. Additionally, to handle nonlinearly separable clusters, we further introduce the proposed competition mechanism into kernel clustering framework. Correspondingly, a new kernel-based competitive learning algorithm which can conduct nonlinear partition without knowing the true cluster number is presented. The promising experimental results on real data sets demonstrate the superiority of the proposed methods.     

Threshold-based clustering with merging and regularization in application to network intrusion detection

Signature-based intrusion detection systems look for known, suspicious patterns in the input data. In this paper we explore compression of labeled empirical data using threshold-based clustering with regularization. The main target of clustering is to compress training dataset to the limited number of signatures, and to minimize the number of comparisons that are necessary to determine the status of the input event as a result. Essentially, the process of clustering includes merging of the clusters which are close enough. As a consequence, we will reduce original dataset to the limited number of labeled centroids. In a complex with k-nearest-neighbor (kNN) method, this set of centroids may be used as a multi-class classifier. The experiments on the KDD-99 intrusion detection dataset have confirmed effectiveness of the above procedure.     

Dynamic clustering using particle swarm optimization with application in image segmentation

A new dynamic clustering approach (DCPSO), based on particle swarm optimization, is proposed. This approach is applied to image segmentation. The proposed approach automatically determines the “optimum” number of clusters and simultaneously clusters the data set with minimal user interference. The algorithm starts by partitioning the data set into a relatively large number of clusters to reduce the effects of initial conditions. Using binary particle swarm optimization the “best” number of clusters is selected. The centers of the chosen clusters is then refined via the K-means clustering algorithm. The proposed approach was applied on both synthetic and natural images. The experiments conducted show that the proposed approach generally found the “optimum” number of clusters on the tested images. A genetic algorithm and random search version of dynamic clustering is presented and compared to the particle swarm version.