spss聚类分析（spss层次聚类分析）

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Preview for next issue: In this issue, we learned the basic knowledge of cluster analysis and discriminant analysis. In the next issue, we will learn the theory and example operation of second-order clustering.

The main methods of statistical research on such problems are cluster analysis and discriminant analysis. As the name implies, the main idea of cluster analysis is that the samples or indicators studied have different degrees of similarity (intimacy). According to this similarity, some samples with greater similarity are aggregated into one category, and other samples with greater similarity are aggregated into another category, forming a classification system from small to large. Finally, draw a pedigree of the whole classification system.

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聚类分析和判别分析的基础知识。

When people recognize a certain kind of things, they often analyze the various objects of this kind of things first in order to find various characteristics of the same kind of things. For example, in the field of national economy, it is sometimes necessary to divide into several sets according to the economic characteristics, industrial structure, GDP, population, per capita income and consumption characteristics of each province. For example, it is divided into economically developed regions and economically underdeveloped regions, resource-rich regions and resource-poor regions. After being divided into such regions, countries can adopt similar economic policies for regions of the same type.

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参考资料：百度百科，《SPSS 23 统计分析实用教程》

SPSS Statistical Analysis (44) Basis of Clustering and Discriminant Analysis

翻译：百度翻译

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Discriminant analysis is a statistical method to determine the type of samples. Like cluster analysis, discriminant analysis is also used to solve various classification problems. The difference is that discriminant analysis establishes a discriminant according to certain criteria on the basis of the known research objects divided into several types and the observed data of a batch of known samples of various types, and then carries out discriminant analysis on samples of unknown types. The discriminant criteria mainly include distance discriminant, Bayes discriminant and Fisher discriminant.

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判别分析是判别样本所属类型的一种统计方法，和聚类分析一样，判别分析也用来解决各种分类问题，不同的是，判别分析在已知研究对象分为若干类型并已取得各种类型的一批已知样本的观测量数据的基础上，根据某种准则建立判别式，然后对未知类型的样本进行判别分析。判别准则主要有距离判别、Bayes判别和Fisher判别等。

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二阶聚类的理论和实例操作。

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而统计学研究这类问题的主要方法是聚类分析和判别分析。顾名思义，聚类分析的主要思想是认为研究的样本或指标之间具有不同程度的相似性（亲疏关系）。根据这种相似性，把一些相似程度较大的样本聚合为一类，把另一些彼此之间相似程度较大的样本又聚合为另一类，形成一个由小到大的分类系统。最后将整个分类系统画成一张谱系图。

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人们认识某类事物时往往会先对这类事物的各个对象进行分析，以便寻找同类事物的各种特征。如在国民经济领域，有时需要根据各省份的经济特点、产业结构、生产总值、人口数量、人均收入、消费特点等分成几个集合。比如分成经济发达区域和经济不发达区域，资源丰富地区和资源匮乏地区等。分成这样的一些地区之后，属于同一类型的区域，国家可以采用相似的经济政策。