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...     

基于文本—链接模型和近邻传播算法的网页聚类*

关于网页聚类的研究已经提出多种基于文本—链接模型的聚类算法,其中应用最广泛的便是MS模型。针对MS模型在效率和计算精度方面的不足,提出了改进的TLMS模型。新模型通过将词聚成词簇、链接向量聚成链接簇的方法将MS模型的词空间和链接空间进行大幅的压缩,并应用近邻传播算法替代传统的K-means算法对网页进行聚类。实验证明,TLMS模型+近邻传播算法聚类精度高、执行效率好。     

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。     

近邻传播半监督聚类算法的分析与改进

近邻传播半监督聚类算法SAP在小数据集上运行时可能会出现并列类代表点的现象,当出现并列类代表点时,依据决策矩阵E对角线上数值大于0确定的类代表点并不是全部的类代表点。分析了近邻传播算法的性质,找出了并列类代表点的出现原因,并针对此现象给出了改进算法。     

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。     

基于近邻传播与密度相融合的进化数据流聚类算法

针对目前数据流离群点不能很好地被处理、数据流聚类效率较低以及对数据流的动态变化不能实时检测等问题,提出一种基于近邻传播与密度相融合的进化数据流聚类算法(I-APDenStream)。此算法使用传统的两阶段处理模型,即在线与离线聚类两部分。不仅引进了能够体现数据流动态变化的微簇衰减密度以及在线动态维护微簇的删减机制,而且在对模型采用扩展的加权近邻传播(WAP)聚类进行模型重建时,还引进了异常点检测删除机制。通过在两种类型数据集上的实验结果表明,所提算法的聚类准确率基本能保持在95%以上,其纯度对比实验等其他相关测试都有较好结果,能够高实效、高质量、高效率地处理数据流数据聚类。     

基于近邻传播聚类和TANE算法的高校数据中函数依赖的发现

针对高校实际数据质量检测过程中数据集存在缺失值以及发现的函数依赖个数较少且不准确的问题,提出了一种结合近邻传播（AP）聚类算法和TANE算法的高校函数依赖发现方法（APTANE）。首先,对数据集中的中文字段进行列剖析,将中文字段值用对应的数值来表示;其次,使用AP聚类算法对数据集中的缺失值进行填补;最后,使用TANE算法从处理好的数据集中自动发现出满足非平凡、最小要求的函数依赖。实验结果表明,在使用AP聚类算法对真实的高校数据集进行修复之后,相比于直接使用函数依赖自动发现算法,发现的函数依赖个数增加到了80个,经过缺失值填补后所发现的函数依赖在表示字段间关联关系时也更加准确,减少了领域专家的工作量,提升了高校数据所拥有数据的质量。     

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 -     

结合稀疏表示与约束传递的半监督谱聚类算法

针对半监督谱聚类不能有效处理大规模数据,没有考虑约束传递不能充分利用有限约束信息的问题,提出一种结合稀疏表示和约束传递的半监督谱聚类算法。首先,根据约束信息生成约束矩阵,将其引入到谱聚类中;然后,将约束集合中的数据作为地标点构造稀疏表示矩阵,近似获得图相似度矩阵,从而改进约束谱聚类模型;同时,根据地标点的相似度矩阵生成连通区域,在每个连通区域内动态调整近邻点,利用约束传递进一步提高聚类准确率。实验表明,所提算法和约束谱聚类相比,在算法效率方面具有明显优势,且准确率没有明显下降;和快速谱聚类方法相比,在聚类准确率上有所提升。