我的第三次作业.doc

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我的第三次作业我的第三次作业

HW3 Part I: written assignment: 1. Cluster the following 8 points into three clusters: A1(2,10), A2(2,5), A3(8,4), A4(5,8), A5(7,5), A6(6,4), A7(1,2), A8(4,9) The distance function is Euclidean distance. Suppose initially we assign A1, A4, A7 as the center of each cluster, respectively. Use the K-Means algorithm to show only The three cluster center after the first round execution 点A2到A1的距离为 5 ; 到A4为 ;到A7为;同理有下表 A1 A4 A7 A2 5 A3 5 A5 A6 A8 由上表可知第一轮后3个集为(1){A1} (2){A3,A4,A5,A6,A8} (3){A2,A7} 他们的中心是(1)(2,10) (2)(6,6) (3)(1.5,3.5) (2)The final three clusters 第二轮 (2,10) (6,6) (1.5,3.5) A1 0 A2 5 A3 A4 A5 A6 2 A7 A8 第二轮后3个集为(1){A1,A8} (2){A3,A4,A5,A6}(3){A2,A7} 更新后 他们的中心是(1)(3,9.5) (2)(6.5,5.25) (3)(1.5,3.5) 第三轮 (3,9.5) (6.5,5.25) (1.5,3.5) A1 A2 A3 A4 A5 A6 A7 A8 第二轮后3个集为(1){A1,A8,A4} (2){A3,A5,A6}(3){A2,A7} 2. Perform AGNES clustering on the data set in Question 1. Show your results by drawing a dendrogram (树状图) (each step merges two clusters with the minimum distance). The dendrogram should clearly show the order in which the points are merged. A1(2,10), A2(2,5), A3(8,4), A4(5,8), A5(7,5), A6(6,4), A7(1,2), A8(4,9) 步骤0 步骤1 步骤2 步骤3 步骤4 步骤5 A1(2,10) {A5,A6} {A4,A5,A6} {A3,A4,A5,A6} {A2,A3,A4,A5,A6} {A1,A2, A3,A4, A5,A6, A7} {A1,A2, A3,A4,A5,A6,A7,A8} A2(2,5) A3(8,4) A4(5,8) A5(7,5) A6(6,4) A7(1,2) A8(4,9) Part II: Lab assignment Production Recommendation Using the “bank-estimation-data”, estimate the decision tree that predicts pep as a function of the other variables. Select “Expert” and set “pruning severity” at 75. Set the “Type” of pep as “Flag” and the “Direction” as “out”. Build decision trees using three options “Minimum records per child branch” values being (a) 56, (b) 15 and (c) 10, not selecting “use global pruning”. Hand in: the confusion matrix for (a), (b) and (c) on the “val

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