Cluster Differences Unfolding for Two-Way Two-Mode Preference Rating Data

Classification and spatial methods can be used in conjunction to represent the individual information of similar preferences by means of groups. In the context of latent class models and using Simulated Annealing, the cluster-unfolding model for two-way two-mode preference rating data has been shown to be superior to a two-step approach of first deriving the clusters and then unfolding the classes. However, the high computational cost makes the procedure only suitable for small or medium-sized data sets, and the hypothesis of independent and normally distributed preference data may also be too restrictive in many practical situations. Therefore, an alternating least squares procedure is proposed, in which the individuals and the objects are partitioned into clusters, while at the same time the cluster centers are represented by unfolding. An enhanced Simulated Annealing algorithm in the least squares framework is also proposed in order to address the local optimum problem. Real and artificial data sets are analyzed to illustrate the performance of the model.     

大数据与当代社会秩序重建——基于历史唯物主义视野

数据是人类对客观世界的认知,人类以数字记录客观事物,通过数学结构探究客观世界的本质及其所蕴含的因果关系。数据在自然界和人类社会中都起到极其重要的作用。现代信息技术的进步,尤其是信息储存技术、信息传播技术以及信息采集技术的进步,产生庞大的数据流,量变引起质变,形成"大数据"。大数据是人类对客观世界整体关系的认知。大数据将改写人类的认知模式、行为方式、社会结构和资源配置的模式,成为一种社会关系的构建力量。人类社会因为大数据而进入新的高速发展阶段。     

Mixtures of Autoregressions with an Improper Component for Panel Data

An EM algorithm for fitting mixtures of autoregressions of low order is constructed and the properties of the estimators are explored on simulated and real datasets. The mixture model incorporates a component with an improper density, which is intended for outliers. The model is proposed as an alternative to the search for the order of a single-component autoregression. The methods can be adapted to other patterns of dependence in panel data. An application to the monthly records of income of the outlets of a retail company is presented.     

Dealing with Distances and Transformations for Fuzzy C-Means Clustering of Compositional Data

Clustering techniques are based upon a dissimilarity or distance measure between objects and clusters. This paper focuses on the simplex space, whose elements??compositions??are subject to non-negativity and constant-sum constraints. Any data analysis involving compositions should fulfill two main principles: scale invariance and subcompositional coherence. Among fuzzy clustering methods, the FCM algorithm is broadly applied in a variety of fields, but it is not well-behaved when dealing with compositions. Here, the adequacy of different dissimilarities in the simplex, together with the behavior of the common log-ratio transformations, is discussed in the basis of compositional principles. As a result, a well-founded strategy for FCM clustering of compositions is suggested. Theoretical findings are accompanied by numerical evidence, and a detailed account of our proposal is provided. Finally, a case study is illustrated using a nutritional data set known in the clustering literature.     

Simultaneous Component and Clustering Models for Three-way Data: Within and Between Approaches

In this paper two techniques for units clustering and factorial dimensionality reduction of variables and occasions of a three-mode data set are discussed. These techniques can be seen as the simultaneous version of two procedures based on the sequential application of k-means and Tucker2 algorithms and vice versa. The two techniques, T3Clus and 3Fk-means, have been compared theoretically and empirically by a simulation study. In the latter, it has been noted that neither T3Clus nor 3Fk-means outperforms the other in every case. From these results rises the idea to combine the two techniques in a unique general model, named CT3Clus, having T3Clus and 3Fk-means as special cases. A simulation study follows to show the effectiveness of the proposal.     

Maximum Likelihood Estimation and Model Comparison for Mixtures of Structural Equation Models with Ignorable Missing Data

The objective of this paper is to develop the maximum likelihood approach for analyzing a finite mixture of structural equation models with missing data that are missing at random. A Monte Carlo EM algorithm is proposed for obtaining the maximum likelihood estimates. A well-known statistic in model comparison, namely the Bayesian Information Criterion (BIC), is used for model comparison. With the presence of missing data, the computation of the observed-data likelihood function value involved in the BIC is not straightforward. A procedure based on path sampling is developed to compute this function value. It is shown by means of simulation studies that ignoring the incomplete data with missing entries gives less accurate ML estimates. An illustrative real example is also presented.     

物联网和智慧城市数据处理与管理概念体系构建 ——以ITU-T数据处理与管理焦点组标准化项目为例

概念体系构建和术语工作是制定任何标准的基础,在多学科和多领域的场景中,构建概念体系和术语工作面临不同利益相关方需求不同而难以达成共识的巨大挑战.文章梳理了ITU-T FG-DPM在促进不同利益相关方和项目组成员之间达成通用概念共识构建统一术语及定义的经验,通过规范概念体系的构建过程,采用术语多维度协同视角来构建统一的数...     

OCLUS: An Analytic Method for Generating Clusters with Known Overlap

The primary method for validating cluster analysis techniques is throughMonte Carlo simulations that rely on generating data with known cluster structure (e.g., Milligan 1996). This paper defines two kinds of data generation mechanisms with cluster overlap, marginal and joint; current cluster generation methods are framed within these definitions. An algorithm generating overlapping clusters based on shared densities from several different multivariate distributions is proposed and shown to lead to an easily understandable notion of cluster overlap. Besides outlining the advantages of generating clusters within this framework, a discussion is given of how the proposed data generation technique can be used to augment research into current classification techniques such as finite mixture modeling, classification algorithm robustness, and latent profile analysis.     

如何驱动企业生态创新?——基于制度理论与利益相关者理论的个案研究

本文以位于浙江省杭州市的一家铅酸电池制造企业为案例,抓住制度压力来源主体,识别影响企业生态创新的关键利益相关者以及他们影响企业生态创新的内在机制,主要结论如下:第一,政府环保导向、客户环保导向、竞争者环保导向、高管环保意识是影响企业生态创新四大主要因素,影响着生态创新的不同维度;第二,政府环保导向、客户环保导向影响企业生态创新的内在机制主要包括规制、规范惩罚以及资源利诱,竞争者环保导向影响企业生态创新的内在机制主要是规制、规范合法性竞赛以及资源争夺竞赛,高管环保意识影响企业生态创新的内在机制则包括道德和利益驱动两个方面。     

Multiple Roles for Analogies in the Genesis of Fluid Mechanics: How Analogies Can Cooperate with Other Heuristic Strategies

When Johann and Daniel Bernoulli founded fluid dynamics they encountered several problems. To go beyond the vision of Newtonian particles, a new set of images was needed in order to deal with the spatial extensibility and lack of form of fluids. I point to evidence that analogy was an essential abductive strategy in the creation of this imagery. But its heuristic behavior is complex: analogy can provide an initial model or proto-model that establishes the starting point of a theoretical process, but it can play other roles as well. The historical genesis analyzed here shows that the participation of analogy in physicists’ creativity is not so restricted and that its richness opens up the field for very different roles and strategies in model-based discovery processes. Analogies can crop up intermittently in the evolution of a theory; and they can cooperate with images, extreme case reasoning and thought experiments, and even activate these processes at origin. Although it may seem that the contributions of analogy are generative in the sense of helping to discover new aspects of reality, we must stress the evaluative function that sometimes is performed by analogical reasoning in order to gain confidence. The study of the Bernoulli’s genesis of the foundations of fluid dynamics generates interesting hypotheses about the multiple roles that analogy can play in scientific model-based reasoning.     

走中国特色的科技自主创新之路是实现经济增长方式转变的战略选择

邓小平同志关于科学技术是第一生产力的精辟论断有其极深刻的内涵,其中最根本的要求,就是要用科技进步推动经济增长方式发生质的转变。从发达国家工业化的进程来看,他们几乎都经历过一个10年左右的突变期,此即经济学家所谓的S型曲线的中段。这一阶段的突出特点是经济发展对科技