产业协同集聚对区域创新效率的空间溢出效应

文章通过引入产业关联系数对产业协同集聚水平的测算方法进行修正,并基于2000—2017年的面板数据,通过空间杜宾模型实证分析了制造业与生产性服务业协同集聚对区域创新效率的空间溢出效应。研究发现：我国制造业与生产性服务业协同集聚水平呈现阶段性波动特征,且协同效应不断加强,共聚趋势逐渐弱化;制造业与生产性服务业协同集聚对区域创新效率的影响呈“倒U”型,且存在显著的空间溢出效应;地理邻近地区产业协同集聚的扩散效应大于回流效应,但经济距离阻碍了产业协同集聚的外溢效果。     

创新要素集聚对区域创新绩效的非线性边际效应演化分析

要素集聚可能存在正负两方面的外部性,充分发挥创新要素集聚的正外部性,对于提升区域创新绩效尤为重要。基于2005—2015年中国省级面板数据,测算了当前各省创新要素集聚水平,运用投影寻踪模型和随机前沿分析技术测算了分省创新绩效,探讨了创新要素集聚对创新绩效的非线性边际效应及其在创新绩效不同分布下的演化特征。研究结果表明:各地区的创新绩效存在较大差异,东部地区的创新绩效明显高于中西部地区,考察期内全国整体创新绩效先降后升,呈现出U型趋势;创新要素集聚与创新绩效存在显著的倒U型关系,且创新要素在不同创新水平下的最优集聚度不同,随着创新绩效的提高,两类创新要素的最优集聚规模整体上呈现出下降趋势。在上述研究结论的基础上,提出相应的政策建议。     

黄河流域制造业集聚对城市绿色经济效率的影响

文章基于2006—2020年黄河流域城市面板数据，运用两步系统GMM和面板门槛模型，实证检验了制造业集聚对城市绿色经济效率的影响。研究发现：（1）流域内制造业集聚对城市绿色经济效率的影响呈现“U”型变化特征，同时，制造业集聚还会扩大城市间绿色经济效率差距。无论是城市绿色经济效率还是城市间绿色经济效率差距均存在路径依赖。（2）制造业集聚对城市绿色经济效率的影响在上、中游地区呈现“U”型变化特征，而在下游地区则表现为正向促进作用；同时，制造业集聚对城市间绿色经济效率差距的扩大效应呈现上、下、中游地区依次递减的变化趋势。（3）流域内制造业集聚对城市绿色经济效率的影响存在基于绿色技术创新水平的双重门槛效应和外商投资水平的单一门槛效应。     

制造业集聚、空间知识溢出与城市创新绩效——来自中国283个城市的证据

制造业是技术创新的重要载体，也是创新驱动发展的主力军。文章基于2009—2019年中国283个地级及以上城市的面板数据，采用空间杜宾模型实证检验了制造业集聚、空间知识溢出对城市创新绩效的影响。研究发现：制造业集聚与城市创新绩效存在先抑制、后促进的“U”型曲线关系；分区域来看，制造业集聚对创新型城市创新绩效存在非线性的促进作用，与非创新型城市创新绩效存在先促进、后抑制的“倒U”型曲线关系；无论是创新型城市还是非创新型城市，制造业集聚对城市创新绩效都存在显著的空间知识溢出效应，即制造业集聚不仅会对本地区城市创新绩效产生影响，而且会通过知识空间溢出影响周围其他城市创新绩效。     

中国体育用品制造业产业集聚与国际竞争力关系的实证分析

在测度中国体育用品制造业产业集聚度和国际竞争力水平这两个指标的基础上,结合钻石模型实证检验了体育用品制造业产业集聚对国际竞争力的影响。检验结果发现：虽然体育用品制造业的产业集聚显著地提升了其国际竞争力,但这种提升存在产品结构上的差异。     

新型城镇化与乡村振兴耦合协调发展的时空格局及影响因素

文章基于新型城镇化与乡村振兴的耦合关系，构建耦合协调发展评价指标体系，采用熵值法、耦合协调度模型测算2011—2018年我国31个省份新型城镇化与乡村振兴的耦合协调度，并结合空间自相关方法和灰色关联度模型探讨二者耦合协调度的空间相关性及影响因素。结果显示：研究期内，二者的耦合协调度呈现上升态势；31个省份的耦合协调度整体上偏低，并呈现“东部>中部>东北>西部”的空间分异特征；二者的耦合协调度具有明显的正向空间自相关性并呈现HH集聚和LL集聚；影响因素按作用强度从大到小依次为基础设施水平、产业结构、经济驱动和政府调控，并且各影响因素对耦合协调度的作用程度具有明显的地区差异。     

制造业产业集聚、环境规制与经济高质量发展

文章基于空间相关性，采用2007—2019年我国30个省份的面板数据，运用空间误差模型，考察制造业产业集聚对经济高质量发展的影响，并分析环境规制的调节作用。研究发现：经济高质量发展与制造业产业集聚水平具有较强的正向空间相关性；在环境规制的调节作用下，制造业产业集聚与经济高质量发展之间呈现“倒U”型关系；对于经济情况不同的地区和技术含量不同的产业，产业集聚对经济高质量发展的影响以及环境规制的作用存在差异；此外，利用门槛效应模型发现，高技术产业集聚对经济高质量发展的影响存在显著的门槛效应。     

制造业集聚与企业绿色全要素生产率：理论分析与经验证据

在绿色发展背景下,文章从理论上分析了制造业集聚对企业绿色全要素生产率的影响,并使用两期Malmquist-Luenberger指数测度了企业绿色全要素生产率,利用中国工业企业微观数据进行了实证检验。研究表明,在考虑能源消耗和环境污染后,制造业集聚的环境负外溢性以及技术锁定效应强于环境正外溢性和技术创新效应,导致制造业集聚对企业绿色全要素生产率产生显著的负向影响。异质性分析表明,相比国有企业和外资企业,制造业集聚对私营企业绿色全要素生产率的负向影响更强。扩展分析表明,制造业集聚对企业绿色技术效率和绿色技术进步均会产生负向影响,并且制造业集聚对企业绿色全要素生产率的负向影响主要是由于制造业集聚阻碍了企业绿色技术进步导致的。     

高新技术产业集聚对生态效率的时空效应

文章基于2009—2018年中国30个省份的面板数据,利用超效率SBM模型测算省域生态效率,分析其时空演变趋势和空间相关性。在此基础上,构建动态空间杜宾模型探讨高新技术产业集聚对生态效率的时空效应。结果表明:生态效率具有显著的时间惯性和空间依赖性。高新技术产业集聚对生态效率的影响存在空间溢出效应,且空间溢出效应在时间上存在差异,即短期内高新技术产业集聚与本地生态效率呈“U”型关系,与邻近地区生态效率呈“倒U”型关系;长期内高新技术产业集聚整体上对生态效率具有促进作用,但未形成显著的溢出效应。     

产业集聚有利于能源效率提升吗

利用中国285个地级及以上城市2004—2013年的统计数据,采用动态空间面板模型分析产业集聚对能源效率的影响。研究结果表明,城市能源效率存在显著的全局空间自相关性和局部空间异质性。从全国层面来看,制造业集聚在没有产生拥塞效应的前提下对能源效率影响为负,生产性服务业集聚和制造业与生产性服务业共同集聚有利于能源效率的提升;从分地区层面来看,制造业集聚、生产性服务业集聚和共同集聚对不同地区能源效率具有显著差异影响。最后,提出改善能源效率的具体建议,如加速产业布局的调整和优化,创造产业良性竞争的集聚环境,减少政府干预等。     

Community detection with structural and attribute similarities

An important problem in network analysis is to identify significant communities. Most of the real-world data sets exhibit a certain topological structure between nodes and the attributes describing them. In this paper, we propose a new community detection criterion considering both structural similarities and attribute similarities. The clustering method integrates the cost of clustering node attributes with the cost of clustering the structural information via the normalized modularity. We show that the joint clustering problem can be formulated as a spectral relaxation problem. The proposed algorithm is capable of learning the degree of contributions of individual node attributes. A number of numerical studies involving simulated and real data sets demonstrate the effectiveness of the proposed method.     

基于高斯谱聚类的风险商户聚类分析

随着电子支付的普及,市场涌现出越来越多的第三方支付平台,而当前关于第三方支付平台商户风险方面的研究相对较少。故本文提出基于高斯谱聚类的风险商户聚类方法,首先使用高斯混合模型构建交易-交易群体的双模网络;其次借助网络中信息传递的思想构建“商户-交易群体网络”的双模网络;再次使用双模网络聚类方法中的谱聚类方法同时对网络中的两类节点聚类,对商户节点聚类的结果可区分出不同风险级别的商户,对交易群体节点聚类的结果可以进一步描述风险商户的交易特征;最后本文分别在模拟数据和某第方支付平台的实际数据中验证了模型的有效性。实验结果表明,本文提出的方法不仅可以准确地区分出不同风险级别的商户群体,而且能总结归纳风险商户的交易特征,为风险商户的监管提供参考。     

A mathematical comparison of the members of an infinite family of agglomerative clustering algorithms

This article presents an algebraic analysis of agglomerative clustering method algorithms, which results in a graphic portrayal of these algorithms and a classification scheme for these algorithms based on the degree of distortion perpetrated on the object space by the algorithms in each group.     

Modular-transform based clustering

Spectral clustering uses eigenvectors of the Laplacian of the similarity matrix. It is convenient to solve binary clustering problems. When applied to multi-way clustering, either the binary spectral clustering is recursively applied or an embedding to spectral space is done and some other methods, such as K-means clustering, are used to cluster the points. Here we propose and study a K-way clustering algorithm – spectral modular transformation, based on the fact that the graph Laplacian has an equivalent representation, which has a diagonal modular structure. The method first transforms the original similarity matrix into a new one, which is nearly disconnected and reveals a cluster structure clearly, then we apply linearized cluster assignment algorithm to split the clusters. In this way, we can find some samples for each cluster recursively using the divide and conquer method. To get the overall clustering results, we apply the cluster assignment obtained in the previous step as the initialization of multiplicative update method for spectral clustering. Examples show that our method outperforms spectral clustering using other initializations.     

An iterative algorithm for optimal variable weighting in K-means clustering

The K-means clustering method is a widely adopted clustering algorithm in data mining and pattern recognition, where the partitions are made by minimizing the total within group sum of squares based on a given set of variables. Weighted K-means clustering is an extension of the K-means method by assigning nonnegative weights to the set of variables. In this paper, we aim to obtain more meaningful and interpretable clusters by deriving the optimal variable weights for weighted K-means clustering. Specifically, we improve the weighted k-means clustering method by introducing a new algorithm to obtain the globally optimal variable weights based on the Karush-Kuhn-Tucker conditions. We present the mathematical formulation for the clustering problem, derive the structural properties of the optimal weights, and implement an recursive algorithm to calculate the optimal weights. Numerical examples on simulated and real data indicate that our method is superior in both clustering accuracy and computational efficiency.     

Studying Complexity of Model-based Clustering

Cluster analysis is a popular statistics and computer science technique commonly used in various areas of research. In this article, we investigate factors that can influence clustering performance in the model-based clustering framework. The four factors considered are the level of overlap, number of clusters, number of dimensions, and sample size. Through a comprehensive simulation study, we investigate model-based clustering in different settings. As a measure of clustering performance, we employ three popular classification indices capable of reflecting the degree of agreement in two partitioning vectors, thus making the comparison between the true and estimated classification vectors possible. In addition to studying clustering complexity, the performance of the three classification measures is evaluated.     

旅游产业的溢出效应及其集群化测度研究——基于山西省11个地市的实证分析

基于2008—2014年的面板数据,以旅游收入和旅游人次为自变量,以地区生产总值为因变量,构建回归模型,对旅游产业的溢出效应进行实证分析。研究发现,旅游收入和旅游人次会对地方经济产生正的溢出效应,适合旅游产业的集群化发展。然后分别运用城市旅游功能、区位熵、产业空间联系方法,通过定性和定量分析,对山西省11个地市的旅游产业集群化程度进行了综合测度与评价,并据此提出了建议。     

Model-based hierarchical clustering with Bregman divergences and Fishers mixture model: application to depth image analysis

Model-based clustering is a method that clusters data with an assumption of a statistical model structure. In this paper, we propose a novel model-based hierarchical clustering method for a finite statistical mixture model based on the Fisher distribution. The main foci of the proposed method are: (a) provide efficient solution to estimate the parameters of a Fisher mixture model (FMM); (b) generate a hierarchy of FMMs and (c) select the optimal model. To this aim, we develop a Bregman soft clustering method for FMM. Our model estimation strategy exploits Bregman divergence and hierarchical agglomerative clustering. Whereas, our model selection strategy comprises a parsimony-based approach and an evaluation graph-based approach. We empirically validate our proposed method by applying it on simulated data. Next, we apply the method on real data to perform depth image analysis. We demonstrate that the proposed clustering method can be used as a potential tool for unsupervised depth image analysis.     

Simultaneous model-based clustering and visualization in the Fisher discriminative subspace

Clustering in high-dimensional spaces is nowadays a recurrent problem in many scientific domains but remains a difficult task from both the clustering accuracy and the result understanding points of view. This paper presents a discriminative latent mixture (DLM) model which fits the data in a latent orthonormal discriminative subspace with an intrinsic dimension lower than the dimension of the original space. By constraining model parameters within and between groups, a family of 12 parsimonious DLM models is exhibited which allows to fit onto various situations. An estimation algorithm, called the Fisher-EM algorithm, is also proposed for estimating both the mixture parameters and the discriminative subspace. Experiments on simulated and real datasets highlight the good performance of the proposed approach as compared to existing clustering methods while providing a useful representation of the clustered data. The method is as well applied to the clustering of mass spectrometry data.     

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.