sas在聚类分析中的应用
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三、CLUSTER 树法变量间聚类,本模型给出了 16 个国家在聚类过程中的具体“中 间”过程，通过树的形式形象而明确的给出了分类的具体结果。
最后对三中模型的优缺点进行对比分析，本文认为各自特点鲜明，且相互补充，而 且聚类结果和实际情况相吻合。 关键字：SAS 聚类分析 距离 VARCLUS FASTCLUS CLUSTER TREE
一、VARCLUS 变量间聚类分析，本模型主要是对变量内的联系进行聚类分析，并 给出了相关的结果：7 个分量分成 5 组，其中 m100 和 m200 分成一组，属于短跑类型； m1500 和 marathon 成为第二类，属于中长跑，而另外三个变量各成一类。
二、FASTCLUS 变量间聚类分析，本模型是对变量间进行聚类分析，得出结果如下： 1 类中仅由西沙摩亚；2 类有阿根廷、百慕大、巴西、智利、哥伦比亚、哥斯达黎加 6 个国家；3 类有库克岛,4 类有澳大利亚、加拿大、杰克斯洛法克、匈牙利、美国、墨西 哥 6 个国家；5 类有多米尼加共和国和危地马拉 2 个国家。5 类实力由强到弱的类的顺 序为 4，2，5，1，3。
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目录
摘 要························································································································································1 一、研究目的 ··············································································································································1 二、采用方法 ··············································································································································1 三、理论知识 ··············································································································································1
SAS 在聚类分析问题中的应用与研究
摘要
本文通过 SAS 软件来解决 1984 年美洲 16 个国家在 7 个项目上的女子记录的聚类 分析问题。
本文首先介绍了一些 SAS 基本知识，然后给出了与聚类分析相关的的理论知识， 比如距离，相关性，并给出了具体的数学表达式。根据这些理论知识结合聚类分析的定 义以及本文的研究目的，本文运用 SAS 中三种比较常用的聚类分析方法对问题进行研 究分析：
3.1 SAS 简介·······································································································································1 3.2 聚类分析定义 ·······························································································································1 3.3 聚类方法分类 ·······························································································································2 3.4 距离的相关定义 ···························································································································2 3.5 相似系数 ·······································································································································3 3.6 类间距离定义 ·······························································································································4 3.7 聚类分析一般步骤························································································································4 四、数据的预处理 ······································································································································5 五、具体模型 ··············································································································································5 5.1 变量聚类分析 ································································································································5
5.1.1 用 VARCLUS 过程实现变量间聚类分析·········································································5 5.1.2 编写程序 ····························································································································6 5.1.3 输出结果 ····························································································································6 5.1.4 结果分析 ····························································································································9 5.2 FASTCLUS 变量间聚类分析 ········································································································9 5.2.1 用 FASTCLUS 进行变量间聚类分析 ················································································9 5.2.2 编写程序 ····························································································································9 5.2.3 输出结果 ··························································································································10 5.2.4 结果分析 ··························································································································10 5.3 CLUSTER 树法变量间聚类分析 ································································································11 5.3.1 CLUSTER 过程简介 ·········································································································11 5.3.2 编写程序 ··························································································································11 5.3.3 输出结果 ··························································································································11 5.3.4 结果分析 ··························································································································13 5.4 三种方法的对比分析··················································································································13 六、参考文献 ············································································································································13 七、附录····················································································································································14 7．1 题目原始数据·················································································································14 7．2 5.2.2 程序的输出结果 ···········ຫໍສະໝຸດ Baidu·······················································································14
最后对三中模型的优缺点进行对比分析，本文认为各自特点鲜明，且相互补充，而 且聚类结果和实际情况相吻合。 关键字：SAS 聚类分析 距离 VARCLUS FASTCLUS CLUSTER TREE
一、VARCLUS 变量间聚类分析，本模型主要是对变量内的联系进行聚类分析，并 给出了相关的结果：7 个分量分成 5 组，其中 m100 和 m200 分成一组，属于短跑类型； m1500 和 marathon 成为第二类，属于中长跑，而另外三个变量各成一类。
二、FASTCLUS 变量间聚类分析，本模型是对变量间进行聚类分析，得出结果如下： 1 类中仅由西沙摩亚；2 类有阿根廷、百慕大、巴西、智利、哥伦比亚、哥斯达黎加 6 个国家；3 类有库克岛,4 类有澳大利亚、加拿大、杰克斯洛法克、匈牙利、美国、墨西 哥 6 个国家；5 类有多米尼加共和国和危地马拉 2 个国家。5 类实力由强到弱的类的顺 序为 4，2，5，1，3。
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目录
摘 要························································································································································1 一、研究目的 ··············································································································································1 二、采用方法 ··············································································································································1 三、理论知识 ··············································································································································1
SAS 在聚类分析问题中的应用与研究
摘要
本文通过 SAS 软件来解决 1984 年美洲 16 个国家在 7 个项目上的女子记录的聚类 分析问题。
本文首先介绍了一些 SAS 基本知识，然后给出了与聚类分析相关的的理论知识， 比如距离，相关性，并给出了具体的数学表达式。根据这些理论知识结合聚类分析的定 义以及本文的研究目的，本文运用 SAS 中三种比较常用的聚类分析方法对问题进行研 究分析：
3.1 SAS 简介·······································································································································1 3.2 聚类分析定义 ·······························································································································1 3.3 聚类方法分类 ·······························································································································2 3.4 距离的相关定义 ···························································································································2 3.5 相似系数 ·······································································································································3 3.6 类间距离定义 ·······························································································································4 3.7 聚类分析一般步骤························································································································4 四、数据的预处理 ······································································································································5 五、具体模型 ··············································································································································5 5.1 变量聚类分析 ································································································································5
5.1.1 用 VARCLUS 过程实现变量间聚类分析·········································································5 5.1.2 编写程序 ····························································································································6 5.1.3 输出结果 ····························································································································6 5.1.4 结果分析 ····························································································································9 5.2 FASTCLUS 变量间聚类分析 ········································································································9 5.2.1 用 FASTCLUS 进行变量间聚类分析 ················································································9 5.2.2 编写程序 ····························································································································9 5.2.3 输出结果 ··························································································································10 5.2.4 结果分析 ··························································································································10 5.3 CLUSTER 树法变量间聚类分析 ································································································11 5.3.1 CLUSTER 过程简介 ·········································································································11 5.3.2 编写程序 ··························································································································11 5.3.3 输出结果 ··························································································································11 5.3.4 结果分析 ··························································································································13 5.4 三种方法的对比分析··················································································································13 六、参考文献 ············································································································································13 七、附录····················································································································································14 7．1 题目原始数据·················································································································14 7．2 5.2.2 程序的输出结果 ···········ຫໍສະໝຸດ Baidu·······················································································14