1) phylogenetic relationship clustering
系统关系聚类分析
2) hierarchical cluster analysis
系统聚类分析
1.
Based on the results, ten kinds of dragon bloods were classified and identified using a hierarchical cluster analysis method.
建立了10种中药血竭样品的HPLC(Highperformanceliquidchromatgraphy)指纹图谱,并把HPLC指纹图谱信息进行数据化及数据标准化处理;用重叠率与相关系数两个参数,从两个方面定量地对这10种样品的HPLC指纹图谱进行了相似性评价;在此基础上用系统聚类分析法定性地对这10种样品进行了分类和鉴别。
2.
In this paper,Fourier transform infrared spectroscopy(FTIR)combined with hierarchical cluster analysis was used to identify 63 samples of Boletus speciosus from five different areas.
用傅里叶变换红外光谱技术结合系统聚类分析了5个不同产地的63个野生小美牛肝菌子实体样本。
3.
According to the land utilization data of Tibet in 2004,through hierarchical cluster analysis of SPSS,this paper comprehensively analyzes the land utilization structure type and social economy development foundation of Tibet,discusses the land utilization district based on the region uniformity.
依据2004年西藏土地利用数据,在全面分析西藏土地利用结构类型和社会经济发展的基础上,结合地域一致性,建立土地利用结构、经济指标体系,运用多元统计软件SPSS的系统聚类分析法,将西藏土地利用划分为藏中综合发展区、藏东农林牧区、藏南农牧区和藏北牧区,并综合分析评述了各分区土地利用现状、经济发展特征及土地利用功能与方向。
3) cluster analysis
系统聚类分析
1.
Eight resistant indexes being used as appraisal standards to rice blast,cluster analysis were conducted on 47 Yunnan local rice varieties and Tetep (a rice variety with durable resistance to rice blast) , Moroberekan (a rice variety which is conceived as durable resistance to rice blast) and B40 (a rice variety with high susceptibility to rice blast).
以 Tetep、Moroberekan、B40为抗、感对照 ,用 8个抗性组分 ,对 47个云南地方稻种资源的抗瘟性进行了系统聚类分析。
2.
This paper uses principal component analysis to know that Collective economy is the most in investment in fixed assets and cluster analysis to classify thirty province.
文章对2006年中国部分省市的有关数据进行统计研究,利用Matlab进行主成分分析和系统聚类分析得出中国部分省市的固定资产投资情况。
3.
Based on the principal components analysis,cluster analysis and overlay analysis,four regions are built.
运用主成分分析、系统聚类分析和叠加分析,将兰州市划为4个分区,结果表明,市内5区与周边3县在经济发展上存在着较大的差异,而生态环境与社会水平上的差异并不显著。
4) system cluster analysis
系统聚类分析
1.
Application of system cluster analysis in classification of farmer household ecosystems.;
系统聚类分析在农户生态系统类型划分中的应用
2.
Based on the field data collected from these cultivars regarding the growth and tree form, the genetic relationship of them was analyzed with the method of the shortest distance in system cluster analysis.
通过对锥栗八个主栽品种的各种特性数量的调查 ,采用系统聚类分析方法中的最短距离法 ,经过计算机分析 ,确定了各个主栽品种之间的亲缘关系 ,对品种鉴别和利用具有一定的理论和实践意义。
3.
The map obtained by nonlinear mapping analysis could well represent the classification of different cities with different levels and the results are much better than that obtained by system cluster analysis.
结果表明,非线性映射方法通过对高维样本进行降维处理,使不同城市化水平等级的城市得以清晰地划分,结果直观形象地显示在二维平面中,从而实现了水平近似城市的聚类,克服了以往系统聚类分析法所得结果不够直观、存在虚假归并等问题,为城市化水平的定量判定提供了一种新方法。
5) hierarchical clustering analysis
系统聚类分析
1.
Hierarchical Clustering analysis of Radix Astragali from different habitats;
不同产地黄芪的系统聚类分析
2.
The 14 indexes of 72 upland soil samples from the poverty area are rationally changed the date by the principal component analysis and hierarchical clustering analysis according to the grading standard of soil nutrients,which are used to build the model for soil nutrient criterion selection and to investigate the soil nutrient status.
以贵州贫困地区为例,依据土壤系统研究法土壤养分分级指标,采用主成分分析-系统聚类分析决策优化模型,对该贫困地区的72个旱作土壤的14项指标进行合理的数据转换,构造土壤养分状况指标筛选模型,聚类分析评价该区的土壤养分状况。
3.
Hierarchical clustering analysis have been done on 48 strains of the representative insects inclusion body viruses (26 strains of nuclear polyhedrosls viruses, 13 strains of granulosis viruses and 9 strains of cytoplasmic polyhedrosis viruses) by capillary gas chromatography, using the eight strategies of hierarchical clustering of Euclidean distance coefficient.
系统聚类分析是统计模式识别的重要方法之一，已广泛应用于化学模式的识别。
6) hierarchical clustering method
系统聚类分析法
1.
By constructing the interval of alternative s attribute value,the experts in the group are classified by the hierarchical clustering method and their weight coefficient is given.
本文对于属性权重信息和属性效用信息都不完全的群体多属性决策问题,通过构造属性值区间和运用系统聚类分析法,对群体决策中的专家进行分类,并确定每位专家的权重。
补充资料：非系统聚类分析
分子式：
CAS号：
性质： 又称非谱系聚类分析。先将各样本粗略分为K个初始类，计算各类形心的坐标，再计算每个样本到类形心的距离，重新将样本聚集到最近距离的类中。再重新计算接受和失去了样本后的各类的形心，再对每个样本进行归类。循此进行，直到每个样本都归到了它与其类形心最靠近的类中，聚类过程停止，最后形成K类。
CAS号：
性质： 又称非谱系聚类分析。先将各样本粗略分为K个初始类，计算各类形心的坐标，再计算每个样本到类形心的距离，重新将样本聚集到最近距离的类中。再重新计算接受和失去了样本后的各类的形心，再对每个样本进行归类。循此进行，直到每个样本都归到了它与其类形心最靠近的类中，聚类过程停止，最后形成K类。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条