有序样品的一些聚类方法

有序样品就是样品的次序不能打乱的样品，在地质勘探，天气预报，天体演化等领域是经常出现的，并且需要将它进行聚类。目前国内外流行的有序样品聚类法是 fisher     

平面有序样品的PP聚类

本文给出平面有序样品的ＰＰ聚类方法，并对ＰＰ指标及Ｐ聚类的性质进行了理论讨论。     

通过同步实现从混沌到有序的转变

本文旨在研究连续的混沌系统是否存在“混沌＋混沌=有序”的现象．证明了两个双向耦合的连续混沌系统在一些情况下可产生有序的动力学行为．作为例子，通过选取适当的耦合参数使Lorenz系统以及Chen和Lee引入的混沌系统同步，进而对同步系统的动力学行为进行了理论分析和数值模拟．结果表明，逐渐改变参数，系统实现了从混沌到有序的过渡．     

有序拓扑向量空间中的共鸣定理

本文研究有序拓扑向量空间中非线性映照的共鸣定理．对于取值于有序拓扑向量空间中的映照，利用序关系，引入了一类广泛的非线性映照．对于这类非线性映照，应用纲理论，并给出了关于点态序有界蕴涵一致序有界的共鸣定理．     

抽象半线性发展方程的正解及应用

本文讨论了有序Ｂａｎａｃｈ空间中的正算子半群的特征，把通常常微分方程及偏微分方程的上、下解方法引入到有序Ｂａｎａｃｈ空间中的半线性发展方程，获得了整体解与正解的存在性．     

基于投影寻踪和最优分割的企业信用评级模型

提出一种基于投影寻踪和最优分割的企业信用评级模型。该模型运用投影寻踪对样本企业进行信用综合评分,将信用综合得分由大到小排序,生成有序样品序列;利用最优分割法对有序样品进行聚类,得出明确的聚类结果;将最优分割点对应的信用综合得分作为划分信用等级的阈值,从而实现对样本企业的信用评级。应用实例证明了该模型的可行性和有效性。     

一个模糊评估模型及其应用(英文)

本文在文［１］，文［２］的基础上，提出了一个适用于质量评估的有序二元对比模糊评估模型．它比现有的方法更切合实际．同时本文论述了在具体操作中所应注意的问题．     

含多重积分的Volterra型积分方程与不等式

本文考虑一定有序局部紧空间上含多重积分的Ｖｏｌｔｅｒｒａ型积分方程，给出了此类方程可解的充分条件，建立了相应的积分不等式．本文结果包含许多已知结论作为特殊情况．     

某些非线性积分不等式

本文考虑一定有序局部紧空间上的非线性积分不等式，并给出其对于一定非线性积分方程的应用．     

关于集值映象方程的一类问题

本文用ｆｐ－同伦方法，在特殊的技巧和边界条件下，研究了有序的（Ｂ）空间中集值凝聚映象方程的正解等问题．     

基于Dirichlet分布有限混合模型的Bayes聚类

本文基于Dirichlet分布有限混合模型,提出了一种用于成分数据的Bayes聚类方法.采用EM算法获得模型参数的估计,用BIC准则确定类数,用类似于Bayes判别的方法对各观测分类.推导了计算公式,编写出程序.模拟研究结果表明,本文提出的方法有较好的聚类效果.     

The influence function of the TCLUST robust clustering procedure

The TCLUST procedure performs robust clustering with the aim of finding clusters with different scatter structures and weights. An Eigenvalues Ratio constraint is considered by TCLUST in order to achieve a wide range of clustering alternatives depending on the allowed differences among cluster scatter matrices. Moreover, this constraint avoids finding uninteresting spurious clusters. In order to guarantee the robustness of the method against the presence of outliers and background noise, the method allows for trimming of a given proportion of observations self-determined by the data. Based on this “impartial trimming”, the procedure is assumed to have good robustness properties. As it was done for the trimmed k-means method, this article studies robustness properties of the TCLUST procedure in the univariate case with two clusters by means of the influence function. The conclusion is that the TCLUST has a robustness behavior close to that of the trimmed k-means in spite of the fact that it addresses a more general clustering approach.     

A tail dependence-based dissimilarity measure for financial time series clustering

In this paper we propose a clustering procedure aimed at grouping time series with an association between extremely low values, measured by the lower tail dependence coefficient. Firstly, we estimate the coefficient using an Archimedean copula function. Then, we propose a dissimilarity measure based on tail dependence coefficients and a two-step procedure to be used with clustering algorithms which require that the objects we want to cluster have a geometric interpretation. We show how the results of the clustering applied to financial returns could be used to construct defensive portfolios reducing the effect of a simultaneous financial crisis.     

Clustering noise-included data by controlling decision errors

Cluster analysis is an unsupervised learning technique for partitioning objects into several clusters. Assuming that noisy objects are included, we propose a soft clustering method which assigns objects that are significantly different from noise into one of the specified number of clusters by controlling decision errors through multiple testing. The parameters of the Gaussian mixture model are estimated from the EM algorithm. Using the estimated probability density function, we formulated a multiple hypothesis testing for the clustering problem, and the positive false discovery rate (pFDR) is calculated as our decision error. The proposed procedure classifies objects into significant data or noise simultaneously according to the specified target pFDR level. When applied to real and artificial data sets, it was able to control the target pFDR reasonably well, offering a satisfactory clustering performance.     

A “Density-Based” Algorithm for Cluster Analysis Using Species Sampling Gaussian Mixture Models

We propose a new model for cluster analysis in a Bayesian nonparametric framework. Our model combines two ingredients, species sampling mixture models of Gaussian distributions on one hand, and a deterministic clustering procedure (DBSCAN) on the other. Here, two observations from the underlying species sampling mixture model share the same cluster if the distance between the densities corresponding to their latent parameters is smaller than a threshold; this yields a random partition which is coarser than the one induced by the species sampling mixture. Since this procedure depends on the value of the threshold, we suggest a strategy to fix it. In addition, we discuss implementation and applications of the model; comparison with more standard clustering algorithms will be given as well. Supplementary materials for the article are available online.     

Clustering by hypergraphs and dimensionality of cluster systems

In the present paper we discuss a new clustering procedure in the case where instead of a single metric we have a family of metrics. In this case we can obtain a partially ordered graph of clusters which is not necessarily a tree. We discuss a structure of a hypergraph above this graph. We propose two definitions of dimension for hyperedges of this hypergraph and show that for the multidimensional p-adic case both dimensions are reduced to the number of p-adic parameters.We discuss the application of the hypergraph clustering procedure to the construction of phylogenetic graphs in biology. In this case the dimension of a hyperedge will describe the number of sources of genetic diversity.     

Multidimensional clustering and hypergraphs

We discuss a multidimensional generalization of the clustering method. In our approach, the clustering is realized by partially ordered hypergraphs belonging to some family. The suggested procedure is applicable in the case where the original metric depends on a set of parameters. The clustering hypergraph studied here can be regarded as an object describing all possible clustering trees corresponding to different values of the original metric.     

A two‐step procedure for clustering time varying data

The aim of this paper is to enlarge the usual domain of cluster analysis. A procedure for clustering time varying data is presented which takes into account the time dimension with its intrinsic properties.

This procedure consists of two steps. In the first step a dissimilarity between variables is defined and the dissimilarity matrix is calculated for each unit separately. In the second step the dissimilarity between units is calculated in terms of the dissimilarity matrices defined in the first step. The dissimilarity matrix obtained is the base for a suitable clustering method.

The procedure is illustrated on an empirical example.     

Model-based clustering of time series in group-specific functional subspaces

This work develops a general procedure for clustering functional data which adapts the clustering method high dimensional data clustering (HDDC), originally proposed in the multivariate context. The resulting clustering method, called funHDDC, is based on a functional latent mixture model which fits the functional data in group-specific functional subspaces. By constraining model parameters within and between groups, a family of parsimonious models is exhibited which allow to fit onto various situations. An estimation procedure based on the EM algorithm is proposed for determining both the model parameters and the group-specific functional subspaces. Experiments on real-world datasets show that the proposed approach performs better or similarly than classical two-step clustering methods while providing useful interpretations of the groups and avoiding the uneasy choice of the discretization technique. In particular, funHDDC appears to always outperform HDDC applied on spline coefficients.     

马氏距离聚类分析中协方差矩阵估算的改进

本文考虑了变量权重和样本类别的影响,建立了马氏距离聚类过程中评估协方差矩阵的迭代法。以Fisher的iris数据为样本,运用欧氏距离一般聚类、主成分聚类、改进前后的马氏距离聚类方法,进行实证分析和比较,结果表明本文所提出的新方法准确率至少提高了6.63%。最后,运用该方法对35个国家的相关指标数据进行聚类分析,确定了各国的卫生保健状况等级。