一种带有导向性的聚类方法在电信客户细分中的应用

聚类是一种常用的客户细分方法。本文结合电信市场客户细分实践,针对一般聚类算法的不足,设计了一种带有导向性的聚类方法。使用该方法得出的聚类结果区分了普通类与极端类,提高了聚类效果,在指导实际市场营销活动中有着明显的优势。     

湖南农业功能拓展思路研究

通过科学制定农业功能分区指导思想与原则,构建科学合理的农业功能分区指标体系,利用聚类分析法和德尔菲法把湖南122个县(市、区)划分为丘岗盆地及洞庭湖平原农产品供给功能主导区、低山丘陵生活保障功能主导区、"四水"上游-南岭山脉生态调节功能主导区、城郊文化传承与休闲观光功能主导区等4大功能主导区,科学提出了各功能主导区的功能定位、功能拓展方向和途径,制定了各功能主导区实现及协调发展的保障性措施。     

农村老年人食品消费影响因素的实证分析——基于江西省调查样本

利用江西省随机调查样本,运用聚类分析法、因子分析法和回归分析对农村老年人食品消费情况及影响因素进行研究。结果发现:(1)新鲜蔬菜在农村老人平常食品消费中属于常规性食品,农村老年人食品消费属于自给型消费。(2)农村老年人的年龄、教育水平、健康状况、婚姻状况、子女是否孝顺、主要经济来源是影响其食品消费的主要影响因素,其中经济来源正向影响最大,其次是子女是否孝顺、教育水平;年龄负向影响最大,其次是健康状况、婚姻状况。     

企业集团、中小企业集群的互动与集群式创新

针对全球价值链发展的现状,以中小企业集群和大企业集团的互动为视角,指出产业集聚区两次转型的历史同时也是二者共同进化的发展过程,产业集聚的发展导致集群式创新的出现,并通过典型案例的分析予以论证.得出应加强中小企业集群与企业集团的互动,以实现集群式创新,进而促进产业升级,控制全球价值链的上游和下游产业集群的结论.     

基于K-均值聚类和泰森多边形的异常值检测方法

离群点发现是数据挖掘研究的一个重要方面.根据数据流的特点提出一种基于K-均值聚类和泰森多边形的离群点检测方法，先用K-均值对数据进行处理，生成中间聚类结果，然后用泰森多边形方法（VOD）对这些中间结果进行再次选择，最后找出可能存在的离群点.     

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。