Knowledge extraction from a large on-line survey: a case study for a higher education corporate marketing

For a higher education public institution, young in relative terms, featuring local competition with another private and both long-established and reputed one, it is of great importance to become a reference university institution to be better known and felt with identification in the society it belongs to and ultimately to reach a good position within the European Higher Education Area. These considerations have made the university governors setting up the objective of achieving an adequate management of the university institutional brand focused on its logo and on image promotion, leading to the establishment of a university shop as it is considered a highly adequate instrument for such promotion. In this context, an on-line survey is launched on three different kinds of members of the institution, resulting in a large data sample. Different kinds of variables are analysed through appropriate exploratory multivariate techniques (symmetrical methods) and regression-related techniques (non-symmetrical methods). An advocacy for such combination is given as a conclusion. The application of statistical techniques of data and text mining provides us with empirical insights about the institution members’ perceptions and helps us to extract some facts valuable to establish policies that would improve the corporate identity and the success of the corporate shop.     

基于自适应迭代更新的函数型数据聚类方法研究

函数型数据的稀疏性和无穷维特性使得传统聚类分析失效。针对此问题,本文在界定函数型数据概念与内涵的基础上提出了一种自适应迭代更新聚类分析。首先,基于数据参数信息实现无穷维函数空间向有限维多元空间的过渡;在此基础上,依据变量信息含量的差异构建了自适应赋权聚类统计量,并依此为函数型数据的相似性测度进行初始类别划分;进一步地,在给定阈值限制下,对所有函数的初始类别归属进行自适应迭代更新,将收敛的优化结果作为最终的类别划分。随机模拟和实证检验表明,与现有的同类函数型聚类分析相比,文中方法的分类正确率显著提高,体现了新方法的相对优良性和实际问题应用中的有效性。     

Implicit Estimation for the Stochastic Volatility Model

Efficient, accurate, and fast Markov Chain Monte Carlo estimation methods based on the Implicit approach are proposed. In this article, we introduced the notion of Implicit method for the estimation of parameters in Stochastic Volatility models.

Implicit estimation offers a substantial computational advantage for learning from observations without prior knowledge and thus provides a good alternative to classical inference in Bayesian method when priors are missing.

Both Implicit and Bayesian approach are illustrated using simulated data and are applied to analyze daily stock returns data on CAC40 index.     

基于购买行为RFM及评论行为RFMP模型的客户终身价值研究

在Web2.0时代,越来越多的消费者在购物网站、点评类网站以及社交类网站上发表自己对产品或服务的相关看法,由此对企业产生了巨大的影响。针对用户在线评论行为所产生的价值,以传统的RFM模型为基础构建了基于评论行为的RFMP模型。同时将购买RFM和评论RFMP模型进行结合,提出了适用于线上企业的客户终身价值评价方法,采用熵值法进行了指标权重的确定,并最终选取大众点评网的实际用户数据进行了传统客户终身价值与改进客户终身价值的对比。通过对用户群进行细分,为企业提供了更加精准的营销决策及管理建议。     

On the strengths of the self-updating process clustering algorithm

The self-updating process (SUP) is a clustering algorithm that stands from the viewpoint of data points and simulates the process how data points move and perform self-clustering. It is an iterative process on the sample space and allows for both time-varying and time-invariant operators. By simulations and comparisons, this paper shows that SUP is particularly competitive in clustering (i) data with noise, (ii) data with a large number of clusters, and (iii) unbalanced data. When noise is present in the data, SUP is able to isolate the noise data points while performing clustering simultaneously. The property of the local updating enables SUP to handle data with a large number of clusters and data of various structures. In this paper, we showed that the blurring mean-shift is a static SUP. Therefore, our discussions on the strengths of SUP also apply to the blurring mean-shift.     

完善研究生创业教育体系的调查与分析——以中南财经政法大学为例

从系统梳理创业能力的国内外文献着手,运用德尔菲法,构建出6个维度的研究生创业胜任力模型。同时,根据对中南财经政法大学研究生的创业意向及创业能力所做的调查与分析,得出研究生创业能力普遍较低、创业实践机会缺乏、创业教育体系需要更加完善等结论。从而提出完善创业教育体系的政策建议：设置创业课程,指导学生制定职业生涯规划;充实和丰富创业教育形式。     

灰色聚类在企业经营业绩评价中的应用——以房地产开发业为例

由于企业经营业绩的评价具有不确定性,运用灰色聚类评估方法对我国房地产企业2008年的经营业绩进行评价,并进行比较分析,结果显示：影响房地产企业经营业绩评价的主要指标是流动资产周转率、资产负债率和营业增长率。针对企业应如何提高房地产企业的经营业绩评价等级问题,提出一些相应的建议。     

企业知识创新能力影响因素分析方法

针对如何提升企业创新能力、提升知识创新水平的问题,分析影响企业知识创新能力的五类因素。利用模糊集理论和模糊聚类方法,进一步提出了影响企业创新能力因素的分析方法。企业应用该方法能够找到影响因素的关键属性集合,根据这些关键属性分析总结出影响企业知识创新能力的关键因素。企业可依据分析结果调整其知识管理和知识创新策略、合理分配组织资源,改善其创新能力并提升其核心竞争力。最后,通过一个案例,进一步说明了该方法在企业中的实际应用。     

跨组织知识整合下的创新网络结构分析

针对跨组织合作形成的创新网络,构建了跨组织知识整合下的创新网络模型,仿真分析得出,组织间互补程度及知识整合效率对跨组织知识整合下的创新网络结构有一定的影响,具体表现为:组织间互补程度及知识整合效率很高时,创新网络具有较短的平均路径长度与较低的集聚系数,此时网络具有随机网络特征;组织间互补程度及知识整合效率很低时,创新网络具有较长的平均路径长度与较高的集聚系数,此时网络具有规则网络的特征;组织间互补程度及知识整合效率大小适度时,创新网络具有相对较短的平均路径长度与较高的集聚系数,因此创新网络具有"小世界"网络的特征。     

Profiles identification on hierarchical tree structure data sets

In this work we study a way to explore and extract more information from data sets with a hierarchical tree structure. We propose that any statistical study on this type of data should be made by group, after clustering. In this sense, the most adequate approach is to use the Mahalanobis–Wasserstein distance as a measure of similarity between the cases, to carry out clustering or unsupervised classification. This methodology allows for the clustering of cases, as well as the identification of their profiles, based on the distribution of all the variables that characterises each subject associated with each case. An application to a set of teenagers' interviews regarding their habits of communication is described. The interviewees answered several questions about the kind of contacts they had on their phone, Facebook, email or messenger as well as the frequency of communication between them. The results indicate that the methodology is adequate to cluster this kind of data sets, since it allows us to identify and characterise different profiles from the data. We compare the results obtained with this methodology with the ones obtained using the entire database, and we conclude that they may lead to different findings.