关于Fuzzy聚类分析及其在脊髓灰质炎中的应用

本文用Fuzzy数学的观点探讨了Fuzzy聚类分析，并通过对脊髓灰质炎病毒抗体水平的Fuzzy聚类分析，获得了良好的聚类效果，为该项问题的研究提供了科学依据。     

Identifying potential binding modes and explaining partitioning behavior using flexible alignments and multidimensional scaling

A method is described for the rapid and automatic analysis of flexible molecular alignments using multidimensional scaling and a normalized scoring scheme. A projection scheme was devised to separate orientational and conformational effects. It is shown that the approach can be utilized for the identification of common binding orientations or to the study of differences in partioning behavior. It is suggested that the method can be employed as a novel approach exploring molecular similarity as a dynamic property, so that it includes aspects of motion (by way of mutual orientations), conformations and molecular properties.     

A Method for Clustering and Screening of Long-dimensional Chemical Data Based on Fingerprints and Similarity Measurements

A method for the treatment of long-dimensional chemical data arrays is presented in this work with the aim of maximising classification models. The method is based on the construction of fingerprints and the subsequent generation of a similarity matrix. The similarity calculation has been modified through a scaling process to take into account different significance shown by the variables. The method was applied to spectral measurements of wines and several aspects were studied, namely: threshold considered in the construction of fingerprints and patterns, weighting factor used for scaling, normalisation method, etc. The application of both Principal Components Analysis and Soft-Independent Modelling of Class Analogies to the similarity matrices gave better classifications of the information than those obtained using original data.     

Partial least-squares regression and fuzzy clustering — A joint approach

Chemical and physical analyses of malt, the main ingredient of beer, have been used to predict the concentration of certain volatile compounds in the finished beer.The prediction was done by means of the partial least squares regression (PLS2) in SIMCA. The total data set as well as individual malt clusters were submitted to PLS analysis. Best prediction was obtained by separating the total object matrix in classes according to similarity found by fuzzy pattern recognition (FCV). FCV was also used to separate the beer variables in classes and to select the subset of variables to be predicted.A joint approach of fuzzy pattern recognition to identify groups of samples and SIMCA-PLS2 to predict several dependent variables is suggested as a powerful tool in process-analytical chemistry.     

Clustering of chemical databases by means of the projection of maximum overlapping sets similarity measurements onto multidimensional spaces

In this paper, we propose a new method for clustering of chemical databases based on the representation of measurements of structural similarity onto multidimensional spaces. The proposed method permits the tuning of the clustering process through the selection of the dimension of the projection space, the normal vectors and the sensibility of the projection process. The structural similarity of each element regarding to the database elements is projected onto the defined spaces generating clusters that represent the characteristics and diversity of the database and whose size and characteristics can be easily adjusted.     

基于模糊C均值聚类的二维直流电阻率与射频大地电磁联合反演

为结合直流电阻率（direct current resistivity, DCR）与射频大地电磁（radio-magnetotelluric, RMT）法反演优势,开展了二维DCR与RMT数据联合反演研究。在经典最小结构模型正则化的基础上,采用平衡算子调节两个数据间的权重,引入模糊C均值（fuzzy C-means, FCM）聚类对电阻率模型进行约束,根据数据均方根误差自动调整FCM聚类项的权重,提高了联合反演效果。通过单独反演与联合反演结果的对比,分析了两种方法的反演能力,总结了联合反演的优势。模型试算表明,DCR与RMT数据联合反演得到的电阻率模型较单独反演更接近实际模型,FCM聚类约束的应用可进一步提高联合反演的效果。     

基于荧光SSR标记的紫薇遗传多样性分析

【目的】以收集的239份紫薇属(Lagerstroemia)种质和1份黄薇属(Heimia)种质为材料,开展种质种间特征分析和紫薇种内资源的遗传多样性分析,为进一步优化紫薇种质资源收集保存及合理利用提供科学依据。【方法】基于16对荧光引物鉴定样品基因型,利用GeneMarker软件进行基因型数据的读取;对239份紫薇属种质和1份黄薇属种质进行特征位点分析;并利用Popgene、Cervus、NTSYS和GenAlEx软件对227份紫薇种质进行遗传多样性参数估算、聚类分析和主成分分析。【结果】紫薇(L.indica)种内的特征位点信息最为丰富,其中15个引物所体现出的特征位点信息中,紫薇都有其独特的位点区别于其他6个种,而在福建紫薇(L.limii)、川黔紫薇(L.excelsa)和尾叶紫薇(L.caudata)中也有部分特异的特征位点是紫薇中没有的;16对紫薇SSR荧光引物共检测出143个等位基因,平均每个位点有8.938个等位基因,平均有效等位基因数(N_e)为3.600,Shannon’s信息指数(I)均值为1.459,观测杂合度(H_o)均...     

自主式交通系统功能架构优化密度峰值聚类算法

自主式交通系统(autonomous transportation system, ATS)是为应对主动式智慧交通发展趋势而提出的新一代交通系统。为科学合理地构建ATS功能架构,提出了一种面向多属性文本的优化密度峰值聚类算法(density peaks clustering, DPC)。该算法结合交通系统功能架构的基本特征,通过改进的词频-逆向文档频率算法与文本向量空间模型,将多属性文本转化成空间维度坐标。再利用高斯函数和决策值优化DPC算法进行聚类,并结合轮廓系数对聚类结果进行评价。为了检验算法的合理性,在ATS道路自动驾驶场景下,基于道路载运工具运行服务域、交通基础设施管理服务域和交通安全管理服务域的功能数据集进行了算例分析,依据聚类结果绘制功能架构图。架构图由自主感知-自主学习-自主决策-自主响应4层构成,验证了ATS应用场景中功能架构优化算法的可行性和合理性。算例结果表明：该算法的构建具有鲁棒性,算例轮廓系数整体均值为0.84,与原算法相比解决了聚类过程中聚类中心难以划定的问题;与原智能交通系统中的各架构设计相比,该功能架构更具有层次性和逻辑性。该优化算法能够促进新一代交通系统...     

土茯苓种苗质量分级标准研究

探究土茯苓(Smilax glabra)种苗质量分级标准,为土茯苓人工种植规范化发展奠定基础。本研究随机选取50株土茯苓种苗进行调查,首先应用相关分析确定苗木分级指标,然后通过逐步聚类分析法修正分级,最终计算出土茯苓种苗的苗木分级标准。结果表明,根数和球茎数为土茯苓种苗的质量分级指标。土茯苓种苗分级标准为Ⅰ级种苗：根数≥37.4,球茎数≥6.8;Ⅱ级种苗：28.3≤根数<37.4,4.2≤球茎数<6.8;Ⅲ级种苗：根数<28.3或球茎数<4.2。所抽取的土茯苓样苗中,Ⅰ、Ⅱ级种苗(合格苗)分别占34%和20%,Ⅲ级种苗(不合格苗)占46%。本研究通过逐步聚类分析法制定的土茯苓种苗质量分级标准结果可靠,说明此方法可行,能为土茯苓人工种植繁育研究以及生产优质的土茯苓种苗提供理论资料。     

基于三支决策的密度敏感谱聚类

将三支决策与密度敏感谱聚类结合,提出了一种基于三支决策的密度敏感谱聚类算法。该算法通过在密度敏感谱聚类的聚类过程引入容差参数得到每个类的上界,然后通过扰动分析算法从上界中分离出核心域,上界和核心域的差值被认定为该类的边界域。聚类结果用核心域和边界域来表示每个类簇,可以更全面地展示数据的结构信息。与传统的硬聚类算法在UCI数据集的实验结果相比较,本文使用核心域计算聚类的评价指标DBI、AS和ACC都有所提升,较好地解决了不确定性对象的聚类问题。