数据挖掘题目及答案

数据挖掘课程设计作业：学号1“book11”中的数据是股票“中金黄金〞的历史交易数据，请根据此训练样本，建立BP神经网络预测模型，并预测“book12”2. “book21”中的数据是三种等级的葡萄酒检验数据，请根据此训练样本，建立BP神经网络分类模型，并预测“book223.用支持向量机方法根据“book31”中训练样本建立工艺参数与“透气量〞之间的预测模型，并预测“book32”数据中对应的4.“book41”中的数据是肿瘤的检验数据，最后一列是肿瘤的性质〔B：良性，M恶性〕，请根据此训练样本，建立BP神经网络分类模型，并预测“book425.“book51”是二分类训练样本，“book521〕按5-最邻近，判别“book52”2〕按3-最邻近，同时使用距离加权方法，判别“book52”3〕按1、3、5-最邻近，再使用二次表决方法，判别“book52”第一题源程序p=[40.584140.440.212037003082617369638.9939.138.8938.4425964924100565958438.5138.938.1138.011711720465638854438.0838.0837.737.431731141065236300837.938.1537.7937.381194714545127142437.7937.8237.7637.01929401134787465637.9538.137.837.4853757432196275237.9137.9937.36371031665138615526436.993735.8635.781519315255054406435.2835.534.1133.81611700055812947234.1734.834.7433.81819218828016038434.1135.5235.2834.11942452232936726434.9934.9933.5633.51307599344596633633.0933.5133.132.31200033439607078432.532.532.2831.1116824673710410563232.5532.531.88602662219474633632.5734.0833.9532.34128430174303219203333.8733.6333793649526504041634.235.5535.3333.991460173350836681635.3335.3335.0534.781020749235747641635.2135.8734.2734.021101695438651529634.334.4934.4733.28834443228340601634.4934.8634.4434.01765290426371872034.235.5935.3934.181746328061302688035.536.4935.6635.452095638075420300835.7536.2936.1435.451515667854481171236.236.4535.9735.71364855149164860836.2537.1936.2236.111854271467826643236.2336.936.6736.231350737249399788837.3838.2936.8336.732383364121488870436.863938.636.8637287772142752486439.139.3838.0937.82239618885844441637.738.237.7536.671691358463262918437.937.9937.7137.021145426842926092837.663837.9637.161550805158140160038.539.3738.7437.7832792106126679731238.7238.7237.3837.351941954073964243237.0137.5537.3436.851031136238300291237.9938.3938.1837.561526272158113068838.238.438.0137.8712773053486719712383837.6437.461344793850563718437.637.637.08371096514940724380836.6637.3637.1436.451517298055727808037.237.5836.9136.81959243935563520036.6936.735.7335.31617522057930016035.336.336.1335.21405994650318864035.536.0935.3635.3976308634718336035.943635.5435.45849206130266214436.1836.4636.3536.021258835445683721636.5137.3637.0936.111673827661663878437.0937.2336.9936.681250277046206956837.437.8837.137.061346698350416995236.8938.9738.3936.8239996664152857305638.2938.337.6337.422543034896294451237.2837.2836.4236.41839143667565811236.437.136.8936.31157246442446806436.9137.3836.4636.16880755732250016036.937.3636.9736.38977750136072320037.537.9637.5937.1219098062717384704]';fori=1:6P(i,:)=(p(i,:)-min(p(i,:)))/(max(p(i,:))-min(p(i,:)));endt=[38.8938.1137.737.7937.7637.837.3635.8634.1134.7435.2833.5633.132.2832.533.9533.6335.3335.0534.2734.4734.4435.3935.6636.1435.9736.2236.6736.8338.638.0937.7537.7137.9638.7437.3837.3438.1838.0137.6437.0837.1436.9135.7336.1335.3635.5436.3537.0936.9937.138.3937.6336.4236.8936.4636.9737.5937.48]';A=(t-min(t))/(max(t)-min(t));p_test=[37.838.1537.4837.451521890157430515237.513837.9337.251876971470850432038.3138.5537.7237.611813770068983232037.437.436.8936.541413008152215888036.737.1837.1436.41854519131482915236.9937.437.136.651006469137206800037.537.8537.2737.081075593140226908837.4938.2537.7437.492216693283867603237.9639.9739.5737.55438464021220744964040.4939.2939.1135051836138899609639.6141.341.0439.454397576219896934440.9540.9539.2439.1137098496147853171238.2538.5237.5537.422154992081538617637.8538.0837.0436.81627416660677305637.8137.9836.9336.91255712546945254437.2837.4437.436.968349168311085824373736.1535.211645015659198156835.635.7934.634.61610259356572902434.0235.0234.4534.021035877835671542433.633.8933.4132.81528849050784105633.5534.2233.9733.3856327728943920034.134.534.4834.1820930428213779234.7834.7934.3934.28585612220219371233.833.833.0932.991108506936996000033.2733.5833.54336619196220021632]';fori=1:6P_test(i,:)=(p_test(i,:)-min(p_test(i,:)))/(max(p_test(i,:))-min(p_test(i,:)));endnet=newff(minmax(P),[6,1],{'tansig','logsig'});net.trainParam.epochs=2000;net.trainParam.goal=0.001;LP.lr=0.1;net=init(net);net=train(net,P,A);temp=sim(net,P);temp1=sim(net,P_test);yuce=temp*(max(t)-min(t))+min(t)yuce1=temp1*(max(t)-min(t))+min(t)运行结果yuce=Columns1through838.890038.291538.353538.204137.934637.794637.359335.9601Columns9through1634.522434.630135.281033.728433.099632.758632.600033.8718Columns17through2433.390035.305435.415234.424134.462634.437735.477735.7399Columns25through3236.597635.224736.079636.737736.583837.804038.088337.7076Columns33through4037.720937.912438.306937.444237.181138.174438.368537.9466Columns41through4837.855436.448736.784635.987035.892735.495535.535536.3506Columns49through5636.708736.967537.391337.680337.572237.046837.111436.8054Columns57through5936.970837.247836.9716yuce1=Columns1through838.383038.622538.567837.976437.455937.704237.707338.7068Columns9through1638.753938.830738.890038.780138.697338.257136.009137.2707Columns17through2437.055834.389636.919432.996535.371735.292735.333735.0161Column2534.2919>>第二题p=[14.231.712.4315.61272.83.060.282.295.641.043.92106513.21.782.1411.21002.652.760.261.284.381.053.4105013.162.362.6718.61012.83.240.32.815.681.033.17118514.371.952.516.8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