[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f8Kyipcr93lP_g51re233yqVnJbuxtyjRv2b7ssm_ebE":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":19,"related":20,"source":30,"type":31},[],"2023-05-10 21:15:45",4778755,[8,9,10,11],"K-Means","决策树","支持向量机","KNN",{"count":13,"courseId":14,"courseImg":15,"courseName":16,"workId":17,"workName":18},19,"93bc172710a2280effe0df1d1d5bb60c","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002Fcd6aa804f56468e1fa7bc4a2057046b9.png","应用数学二","7c4ee1b89aee48bf951d74c752281ba5","","以下哪些方法属于无监督算法",[21,32,41,44,53,62,71,80,89,98],{"answer":22,"createTime":5,"id":23,"options":24,"question":29,"source":30,"type":31},[],4778753,[25,26,27,28],"圆形分布","螺旋分布","带状分布","凸多边形分布","K-Means 算法无法聚以下哪种形状的样本","v1",0,{"answer":33,"createTime":5,"id":34,"options":35,"question":40,"source":30,"type":31},[],4778754,[36,37,38,39],"1","19","6","\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F90d19876b7c3fbd160d034ee78b5cb4f.webp\">","向量 X=[1,2,3,4,-9,0] 的 L1 范数为",{"answer":42,"createTime":5,"id":6,"options":43,"question":19,"source":30,"type":31},[],[8,9,10,11],{"answer":45,"createTime":5,"id":46,"options":47,"question":52,"source":30,"type":31},[],4778756,[48,49,50,51],"聚类算法是对给定数据通过算法判断数据属于已分好的类中的具体哪一类","k-means聚类是事先给出原始数据所含的类数,然后将含有相似特征的数据聚为一个类中","聚类分析是研究对样品或变量(指标)进行分类的一种一元统计方法","聚类分析有两种,一种是对样品的分类,称为R型,另一种是对变量(指标)的分类,称为Q型","下列说法中正确的是",{"answer":54,"createTime":5,"id":55,"options":56,"question":61,"source":30,"type":31},[],4778757,[57,58,59,60],"常用的刻画相似度的统计量有距离和相似系数两种","距离多用于样品的分类,相似系数多用于指标的分类","指标i与指标j之间的相似系数越接近于0,说明指标i与指标j关系越密切;彼此无关的指标间的相似系数越接近于1,完全相反的指标相似系数为-1","通常各样品或指标的观测值因量纲不同或数量级不同,需要对原始数据进行标准化","下列说法中不正确的是",{"answer":63,"createTime":5,"id":64,"options":65,"question":70,"source":30,"type":31},[],4778758,[66,67,68,69],"squaredeuclidean","euclidean","seuclidean","cityblock","成对观测量之间的两两距离计算函数pdist()默认采用的距离计算方法采用的是",{"answer":72,"createTime":5,"id":73,"options":74,"question":79,"source":30,"type":31},[],4778759,[75,76,77,78],"D1==D2","D1≠D2","D1与D2没有直接关系","无法判断","分析以下的代码段:clear;clc;rng('default') %for reproducibilityX = rand(3,2);D1 = pdist(X,'minkowski',1);D2 = pdist(X,'cityblock');其中D1与D2的关系正确的是",{"answer":81,"createTime":5,"id":82,"options":83,"question":88,"source":30,"type":31},[],4778760,[84,85,86,87],"\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002Fa24c21d7c1f57de395d28eae56a176eb.png\">","\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F21932ea0fd66421064491cd0284c381f.png\">","\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F5718f2149383fb2155959167e3f9e35e.png\">","\u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002Fdfb41b2654995921941d1a5a3f7457d3.png\">","对3x2矩阵X使用D = pdist(X)计算的样本间的欧式距离D = [0.2954 1.0670 0.9448],则Z = squareform(D),得到的Z是",{"answer":90,"createTime":5,"id":91,"options":92,"question":97,"source":30,"type":31},[],4778761,[93,94,95,96],"Zx和Zy都是对称矩阵","Zx是对称矩阵,Zy不是对称矩阵","Zy是对称矩阵,Zx不是对称矩阵","Zx和Zx都不是对称矩阵","对3x2矩阵X与矩阵Y中的数据进行如下的代码运算Dx = pdist(X);Dy = pdist(Y);Zx = squareform(Dx);Zy = squareform(Zx);以下表述正确的是",{"answer":99,"createTime":5,"id":100,"options":101,"question":106,"source":30,"type":31},[],4778762,[102,103,104,105],"模糊C均值聚类算法是一个不断迭代计算隶属度和簇中心的过程,直到他们达到最优","样本 \u003Cimg src=\"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F13bc0c955a9b6bab4b53a8cfaad17332.png\">对于每个簇的隶属度之和为1","模糊C均值聚类算法需要预先设定分类的簇数","模糊C均值聚类算法的默认最大迭代次数是1000","关于模糊C均值聚类算法,错误的叙述是"]