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.     

虚拟团队关键成功因素模型构建与实证研究

在对虚拟团队相关文献深入检索与分析的基础上,归纳、提炼出影响虚拟团队成功的15项关键因素,构建了虚拟团队关键成功因素三维(目标、成员、沟通)模型假设;通过对国内12家IT企业的问卷调查,收集到242条有效数据;利用描述性统计分析对15项关键因素进行了验证,利用R型因子聚类分析对三维模型假设进行验证,结果表明15项关键成功因素体现在目标一致性、信息技术能力、成员素质、领导能力和沟通协调能力五个维度上;最后,从这五个方面出发,给出了提升虚拟团队绩效的针对性策略.     

我国人口转变地区差异及其与社会经济发展的相关分析

我国的人口转变已经完成 ,但是地区差异颇大。通过对各省份 1949— 1999年人口自然变动系列资料的聚类分析 ,可以把我国的人口转变类型划分为都市、东部、中部、西部和西藏 5种模式 ,各地区人口转变过程及其特点与其社会经济发展水平高度相关     

Monitoring point patterns for the development of space–time clusters

Existing statistical methods for the detection of space–time clusters of point events are retrospective, in that they are used to ascertain whether space–time clustering exists among a fixed number of past events. In contrast, prospective methods treat a series of observations sequentially, with the aim of detecting quickly any changes that occur in the series. In this paper, cumulative sum methods of monitoring are adapted for use with Knox's space–time statistic. The result is a procedure for the rapid detection of any emergent space–time interactions for a set of sequentially monitored point events. The approach relies on a 'local' Knox statistic that is useful in retrospective analyses to detect when and where space–time interaction occurs. The distribution of the local Knox statistic under the null hypothesis of no space–time interaction is derived. The retrospective local statistic and the prospective cumulative sum monitoring method are illustrated by using previously published data on Burkitt's lymphoma in Uganda.     

A fuzzy clustering approach to evaluate individual competencies from REFLEX data

We empirically illustrate how concepts and methods involved in a grade of membership (GoM) analysis can be used to sort individuals by competence. Our study relies on a data set compiled from the international survey on higher education graduates called REFLEX. We focus on the subset of data related to the perception of own competencies. It is first decomposed into fuzzy clusters that form a hierarchical fuzzy partition. Then, we calculate a scalar measure of competencies for each fuzzy cluster, and subsequently use the individual GoM scores to combine cluster-based competencies to position individuals on a scale from 0 to 1.     

Strong Consistency of Reduced K‐means Clustering

Reduced k‐means clustering is a method for clustering objects in a low‐dimensional subspace. The advantage of this method is that both clustering of objects and low‐dimensional subspace reflecting the cluster structure are simultaneously obtained. In this paper, the relationship between conventional k‐means clustering and reduced k‐means clustering is discussed. Conditions ensuring almost sure convergence of the estimator of reduced k‐means clustering as unboundedly increasing sample size have been presented. The results for a more general model considering conventional k‐means clustering and reduced k‐means clustering are provided in this paper. Moreover, a consistent selection of the numbers of clusters and dimensions is described.     

A cluster tree based model selection approach for logistic regression classifier

Model selection methods are important to identify the best approximating model. To identify the best meaningful model, purpose of the model should be clearly pre-stated. The focus of this paper is model selection when the modelling purpose is classification. We propose a new model selection approach designed for logistic regression model selection where main modelling purpose is classification. The method is based on the distance between the two clustering trees. We also question and evaluate the performances of conventional model selection methods based on information theory concepts in determining best logistic regression classifier. An extensive simulation study is used to assess the finite sample performances of the cluster tree based and the information theoretic model selection methods. Simulations are adjusted for whether the true model is in the candidate set or not. Results show that the new approach is highly promising. Finally, they are applied to a real data set to select a binary model as a means of classifying the subjects with respect to their risk of breast cancer.     

审丑时代的来临--大众文化视域下审美形态研究

在大众文化背景下，丑的表现形态多种多样，丑迎合了大众“审美疲劳”的神经，由边缘逐渐走向中心。美学研究当然不能排斥丑，从传统的审美模式到当下审丑的泛滥，离不开大众文化提供的种种条件，商业利益的驱动、全球信息一体化的实现以及更包容的道德态度，都催生着丑，传播着丑。丑以一种碎片化、平面化、欲望化、狂欢化的姿态呈现在大众面前，这在一定程度上丰富了大众的审美视野，促使审美领域从一元向多元迈进。唯有理性的看待丑，才能树立正确的审丑观。     

Fuzzy duocentric community detection model in social networks

The main goal of this paper is to present a clustering model to identify duocentric communities in the complex networks. A duocentric community is built around two central nodes which are as close as possible to other nodes, while the central nodes are connected enough to each other to shape the center of the community. To detect such communities, we develop a new objective function based clustering model. The network's nodes are assigned to the duocentric communities by the type-2 fuzzy numbers which indicate the degrees of belonging to the communities by upper and lower membership values. Generated interval type-2 fuzzy membership values by our proposed model are able to determine how much each node belongs to the both central nodes and how it is shared among communities. Also, the compatible verification index with the proposed model is introduced to evaluate and compare the results of the proposed model with the existing approach in the literature. Finally, the performance of the proposed algorithm is validated by detecting duocentric communities in three artificial networks and two real social networks.     

世俗化:当代中国政治文化发展之趋向

作者认为,随着改革开放和社会主义现代化建设的深入开展,当代中国的政治文化发展正经历着一个世俗化的过程,这一过程具有现世性、理性化、参与性、开放性的特征。现代化的推进以及市场经济体制的逐步建立和发展,为中国政治文化世俗化的发展提供了现实的基础;然而,中国社会转型时期不稳定因素,造成中国目前世俗化发展过程中的缺陷。