云制造环境下基于本体和模糊QoS的供应商匹配方法

云制造环境下的服务匹配具有资源数量大、语义信息不对称、QoS多样化和模糊化的特点,同时企业有自主选择匹配结果的需求。为此,提出基于本体和模糊QoS聚类的三阶段供应商匹配模型。首先构建本体模型和供应商服务描述模型,运用语义本体既消除了信息的不对称性,又增加了语义信息的完整性。此外,对QoS的多属性信息进行三角模糊化处理,结合模糊偏好和优化的模糊C均值聚类（FCM）算法按需聚类,提高了收敛速度和精度,得出基于匹配度排序的结果集合。实例验证结果表明：本文匹配方法较传统方法有更高的适应性和查准率。     

The cluster correlation-network support vector machine for high-dimensional binary classification

ABSTRACT

Identifying homogeneous subsets of predictors in classification can be challenging in the presence of high-dimensional data with highly correlated variables. We propose a new method called cluster correlation-network support vector machine (CCNSVM) that simultaneously estimates clusters of predictors that are relevant for classification and coefficients of penalized SVM. The new CCN penalty is a function of the well-known Topological Overlap Matrix whose entries measure the strength of connectivity between predictors. CCNSVM implements an efficient algorithm that alternates between searching for predictors’ clusters and optimizing a penalized SVM loss function using Majorization–Minimization tricks and a coordinate descent algorithm. This combining of clustering and sparsity into a single procedure provides additional insights into the power of exploring dimension reduction structure in high-dimensional binary classification. Simulation studies are considered to compare the performance of our procedure to its competitors. A practical application of CCNSVM on DNA methylation data illustrates its good behaviour.     

Mixtures of general location model with factor analyzer covariance structure for clustering mixed type data

Cluster analysis is one of the most widely used method in statistical analyses, in which homogeneous subgroups are identified in a heterogeneous population. Due to the existence of the continuous and discrete mixed data in many applications, so far, some ordinary clustering methods such as, hierarchical methods, k-means and model-based methods have been extended for analysis of mixed data. However, in the available model-based clustering methods, by increasing the number of continuous variables, the number of parameters increases and identifying as well as fitting an appropriate model may be difficult. In this paper, to reduce the number of the parameters, for the model-based clustering mixed data of continuous (normal) and nominal data, a set of parsimonious models is introduced. Models in this set are extended, using the general location model approach, for modeling distribution of mixed variables and applying factor analyzer structure for covariance matrices. The ECM algorithm is used for estimating the parameters of these models. In order to show the performance of the proposed models for clustering, results from some simulation studies and analyzing two real data sets are presented.     

The sequence analysis of hospitals that established social work department under fuzzy environment

This paper reviews the existing literature on hospital social work and discusses intervention strategies for improving social work practice in hospital. The objective of this study was to improve the quality of medical care. But few studies have compared social work services between different hospitals. This study describes qualitative analysis under fuzzy environment, extracts the main influencing factors and establishes a comprehensive evaluation index system. It provides comprehensive evaluation for alternative hospitals by the fuzzy clustering method. This paper proposes a new mixed fuzzy clustering algorithm on the basis of analysing the axiomatic fuzzy set (AFS) and K-means algorithm, which is not affected by some complicated parameter issues and has higher statistical validity. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied for selecting the best option for each cluster and a comparative analysis is done. Results from a case study in Shanghai, China, confirm that the proposed approach is effective by using information entropy to test. By comparing AFS, K-means and C-means algorithms, the hybrid algorithm can find the two closest attributes of evaluation index of hospital social work, and the proposed approach can be easily help raise the level of hospital social work service.     

WSNs中基于协作通信的分簇路由算法研究

针对无线传感器网络数据信息传输可靠性,提出一种无线传感器网络中基于协作通信的分簇路由算法。该算法主要对分簇、簇间路由和簇内路由三个阶段进行了设计,依据信噪比寻找满足网络数据传输可靠性的最佳路由,实现源节点和目的节点之间的协作通信。仿真结果表明,无线传感器网络中基于协作通信的分簇路由算法能够有效地优化网络路径,提高频谱利用率,增强数据信息传输的可靠性。     

Irreconcilable differences? Ethnic intermarriage and divorce in the Netherlands, 1995-2008

This study uses population data of the Netherlands (municipality registers) between 1995 and 2008 to describe and explain the occurrence of divorce among recently newlywed interethnic and mono-ethnic couples (N = 116,745). In line with homogamy theory, divorce risks are higher for interethnic couples, in particular if the spouses were born and raised in countries that are culturally distant from each other. In addition, the effect of cultural distance is smaller for second generation immigrants than for first generation immigrants. There is no evidence for a higher risk of divorce among Black-White marriages. In line with convergence theory, results show that the higher the divorce propensity in the wife’s origin country, the higher the divorce risk of a couple is.     

A finite mixture approach to joint clustering of individuals and multivariate discrete outcomes

In this work, we modify finite mixtures of factor analysers to provide a method for simultaneous clustering of subjects and multivariate discrete outcomes. The joint clustering is performed through a suitable reparameterization of the outcome (column)-specific parameters. We develop an expectation–maximization-type algorithm for maximum likelihood parameter estimation where the maximization step is divided into orthogonal sub-blocks that refer to row and column-specific parameters, respectively. Model performance is evaluated via a simulation study with varying sample size, number of outcomes and row/column-specific clustering (partitions). We compare the performance of our model with the performance of standard model-based biclustering approaches. The proposed method is also demonstrated on a benchmark data set where a multivariate binary response is considered.     

Comparing volatility forecasting models during the global financial crisis

Volatility estimation in financial markets has always been a challenge especially in time of crisis. Once asset prices and investment decisions are highly sensitive to such variable, many different models have been proposed in literature. This article estimates the volatility from a new family of stochastic volatility models called non-Gaussian State Space Models, a subclass of state space models where it is possible to compute exact likelihood. Volatilities of important Asian and Oceanian stock market indexes have been estimated and compared to APARCH model estimates. Results showed that non-Gaussian State Space Models outperformed significantly in both in-sample and forecasting cases.     

A new approach: Interrelated two-way clustering of gene expression data

The paper presents a new approach to interrelated two-way clustering of gene expression data. Clustering of genes has been effected using entropy and a correlation measure, whereas the samples have been clustered using the fuzzy C-means. The efficiency of this approach has been tested on two well known data sets: the colon cancer data set and the leukemia data set. Using this approach, we were able to identify the important co-regulated genes and group the samples efficiently at the same time.