实验六--各国家和地区男子径赛记录数据的聚类分析

实验六 各国家和地区男子径赛记录数据的聚类分析一、实验目的1.掌握如何使用SAS软件来进行聚类分析；2.看懂和理解SAS输出的结果，并学会以此来作出分析；3.对同一组数据使用五种系统聚类方法，学会对各种聚类效果的比较，获取重要经验；二、实验内容实验1 下表中列出各国家和地区男子径赛记录的数据，试分别用类平均法、离差平方和法和k均值法进行聚类分析，聚类前先对各变量做标准化变换。国家和地区Alphabet100米（秒）200米（秒）400米（秒）800米（秒）1500米（秒）5000米（秒）10000米（秒）马拉松阿根廷a10.3920.8146.841.813.714.0429.36137.72澳大利亚a10.3120.0644.841.743.5713.2827.66128.3奥地利a10.4420.8146.821.793.613.2627.72135.9比利时b10.3420.6845.041.733.613.2227.45129.95百慕大b10.2820.5845.911.83.7514.6830.55146.62巴西b10.2220.4345.211.733.6613.6228.62133.13缅甸b10.6421.5248.31.83.8514.4530.28139.95加拿大c10.1720.2245.681.763.6313.5528.09130.15智利c10.3420.846.21.793.7113.6129.3134.03中国c10.5121.0447.31.813.7313.929.13133.53哥伦比亚c10.4321.0546.11.823.7413.4927.88131.35库克群岛c12.1823.252.942.024.2416.735.38164.7哥斯达黎c10.9421.948.661.873.8414.0328.81136.58捷克斯洛c10.3520.6545.641.763.5813.4228.19134.32丹麦d10.5620.5245.891.783.6113.528.11130.78多米尼加d10.1420.6546.81.823.8214.9131.45154.12芬兰f10.4320.6945.491.743.6113.2727.52130.87法国f10.1120.3845.281.733.5713.3427.97132.3德意志民g10.1220.3344.871.733.5613.1727.42129.92德意志联g10.1620.3744.51.733.5313.2127.61132.23大不列颠g10.1120.2144.931.73.5113.0127.51129.13希腊g10.2220.7146.561.783.6414.5928.45134.6危地马拉g10.9821.8248.41.893.814.1630.11139.33匈牙利h10.2620.6246.021.773.6213.4928.44132.58印度ii10.621.4245.731.763.7313.7728.81131.98印度尼西i10.5921.4947.81.843.9214.7330.79148.83以色列i10.6120.9646.31.793.5613.3227.81132.35爱尔兰i10.712147.81.773.7213.6628.93137.55意大利i10.0119.7245.261.733.613.2327.52131.08日本j10.3420.8145.861.793.6413.4127.72128.63肯尼亚k10.4620.6644.921.733.5513.127.38129.75韩国k10.3420.8946.91.793.7713.9629.23136.25朝鲜人民k10.9121.9447.31.853.7714.1329.67130.87卢森堡l10.3520.7747.41.823.6713.6429.08141.27马来西亚m10.420.9246.31.823.814.6431.01154.1毛里求斯m11.1922.4547.71.883.8315.0631.77152.23墨西哥m10.4221.346.11.83.6513.4627.95129.2荷兰n10.5220.9545.11.743.6213.3627.61129.02新西兰n10.5120.8846.11.743.5413.2127.7128.98挪威n10.5521.1646.711.763.6213.3427.69131.48巴布亚新p10.9621.7847.91.94.0114.7231.36148.22菲律宾p10.7821.6446.241.813.8314.7430.64145.27波兰p10.1620.2445.361.763.613.2927.89131.58葡萄牙p10.5321.1746.71.793.6213.1327.38128.65罗马尼亚r10.4120.9845.871.763.6413.2527.67132.5新加坡s10.3821.2847.41.883.8915.1131.32157.77西班牙s10.4220.7745.981.763.5513.3127.73131.57瑞士s10.2520.6145.631.773.6113.2927.94130.63瑞典s10.3720.4645.781.783.5513.2227.91131.2台北t10.5921.2946.81.793.7714.0730.07139.27泰国t10.3921.0947.911.833.8415.2332.56149.9土耳其t10.7121.4347.61.793.6713.5628.58131.5美国u9.9319.7543.861.733.5313.227.43128.22苏联u10.072044.61.753.5913.227.53130.55西萨摩亚w10.8221.86492.024.2416.2834.71161.83三、实验要求1.完成三种系统聚类方法，比较其聚类效果；2.能对试验结果进行合理分析.四、实验内容4.1完成三种系统聚类方法，比较其聚类效果4.1.1导入数据集exec654.1.2类平均法聚类proc cluster data=work.exec65 method=ave nosquare std; id nation;proc tree horizontal; id nation;run; 答：用类平均法聚类的话认为较合适的是聚成4类，但分成两类以上的话，库克群岛和西萨摩亚都会单聚1类，可知用类平均法的聚集效果一般。4.1.3离差平方和法聚类 答：用离差平方和法聚类认为较适合的是聚成4类，但库克群岛和西萨摩亚聚成1类，聚集效果尚可。4.1.4 k均值法聚类初始凝聚点：观测所归类及与最终凝聚点的距离：聚类汇总：各变量的类内均值及标准差：答：用k均值法聚类的话认为较合适的是聚成5类，但库克群岛和西萨摩亚都单成1类，聚集效果一般。4.2对试验结果进行合理分析从类平均、离差平方和、k均值三种聚类方