数据挖掘实验报告.docx

1、数据挖掘实验报告数据挖掘实验报告实验序号：实验项目名称：C4.5算法学号姓名专业、班12数学金融实验地点实验楼5-510指导教师潘巍巍实验时间2014.12.24一、实验目的及要求1：选择一个数据挖掘标准数据集，采用C4.5算法进行分类，给出分类精度，画出用C4.5算法诱导的树并写出生成的规则集合。2：在数据挖掘标准数据集上，实验对比剪枝与未剪枝的树的分类性能。3：总结C4.5算法的优缺点二、实验设备（环境）及要求 电脑 WEKA 3.6.13、实验内容与步骤（3）数据分类（c4.5算法实现） 1.导入数据 （2）选择C4.5分类器进行分类结果为其中分类精度为50%生成的决策树为 分类规则：J

2、48 pruned tree-outlook = sunny| humidity = high: no (3.0)| humidity = normal: yes (2.0)outlook = overcast: yes (4.0)outlook = rainy| windy = TRUE: no (2.0)| windy = FALSE: yes (3.0)剪枝后结果为分类精度变为57.1% 性能变好（1）C4.5算法优缺点优点： 分类精度高，生成的分类规则比较简单，易于理解。缺点： 需要多次扫描数据集，比较低效 五、分析与讨论 六、教师评语签名：日期：成绩数据挖掘实验报告实验序号：实验项目

3、名称：KNN算法学号姓名专业、班12数学金融实验地点实验楼5-510指导教师潘巍巍实验时间2014.12.241、实验目的及要求1：KNN算法的基本思路、步骤。2：选择UCI中的5个标准数据集，使用KNN算法在该数据集上计算混淆矩阵。3：选择2个数据集，选择不同的k值，k=1,3,5,7,9，对比KNN算法计算结果的差异。二、实验设备（环境）及要求 电脑 WEKA 3.6.14、实验内容与步骤 1.数据集 contact-lenses.arff Glass.arff两者的混淆矩阵分别为(2)两个数据集在K=1,3,5,7,9下结果分别为Glass：K=1;= Summary =Correctl

4、y Classified Instances 151 70.5607 %Incorrectly Classified Instances 63 29.4393 %Kappa statistic 0.6005Mean absolute error 0.0897Root mean squared error 0.2852Relative absolute error 42.3747 %Root relative squared error 87.8627 %Total Number of Instances 214 = Detailed Accuracy By Class = TP Rate FP

5、 Rate Precision Recall F-Measure ROC Area Class 0.786 0.167 0.696 0.786 0.738 0.806 build wind float 0.671 0.13 0.739 0.671 0.703 0.765 build wind non-float 0.294 0.051 0.333 0.294 0.313 0.59 vehic wind float 0 0 0 0 0 ? vehic wind non-float 0.769 0.03 0.625 0.769 0.69 0.895 containers 0.778 0.015 0

6、.7 0.778 0.737 0.838 tableware 0.793 0.011 0.92 0.793 0.852 0.884 headlampsWeighted Avg. 0.706 0.109 0.709 0.706 0.704 0.792= Confusion Matrix = a b c d e f g - classified as 55 9 6 0 0 0 0 | a = build wind float 15 51 4 0 3 2 1 | b = build wind non-float 9 3 5 0 0 0 0 | c = vehic wind float 0 0 0 0

7、 0 0 0 | d = vehic wind non-float 0 2 0 0 10 0 1 | e = containers 0 1 0 0 1 7 0 | f = tableware 0 3 0 0 2 1 23 | g = headlampsK=3;= Summary =Correctly Classified Instances 154 71.9626 %Incorrectly Classified Instances 60 28.0374 %Kappa statistic 0.6097Mean absolute error 0.0983Root mean squared erro

8、r 0.2524Relative absolute error 46.4438 %Root relative squared error 77.7792 %Total Number of Instances 214 = Detailed Accuracy By Class = TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0.843 0.215 0.656 0.843 0.738 0.865 build wind float 0.711 0.138 0.74 0.711 0.725 0.835 build wind non-

9、float 0.176 0.015 0.5 0.176 0.261 0.672 vehic wind float 0 0 0 0 0 ? vehic wind non-float 0.615 0.015 0.727 0.615 0.667 0.913 containers 0.778 0.01 0.778 0.778 0.778 0.914 tableware 0.793 0.011 0.92 0.793 0.852 0.885 headlampsWeighted Avg. 0.72 0.123 0.718 0.72 0.708 0.847= Confusion Matrix = a b c

10、d e f g - classified as 59 10 1 0 0 0 0 | a = build wind float 19 54 2 0 1 0 0 | b = build wind non-float 10 4 3 0 0 0 0 | c = vehic wind float 0 0 0 0 0 0 0 | d = vehic wind non-float 0 3 0 0 8 0 2 | e = containers 0 1 0 0 1 7 0 | f = tableware 2 1 0 0 1 2 23 | g = headlampsK=5;= Summary =Correctly

11、 Classified Instances 145 67.757 %Incorrectly Classified Instances 69 32.243 %Kappa statistic 0.5469Mean absolute error 0.1085Root mean squared error 0.2563Relative absolute error 51.243 %Root relative squared error 78.9576 %Total Number of Instances 214 = Detailed Accuracy By Class = TP Rate FP Rat

12、e Precision Recall F-Measure ROC Area Class 0.843 0.229 0.641 0.843 0.728 0.867 build wind float 0.684 0.174 0.684 0.684 0.684 0.848 build wind non-float 0 0.01 0 0 0 0.642 vehic wind float 0 0 0 0 0 ? vehic wind non-float 0.385 0.025 0.5 0.385 0.435 0.952 containers 0.667 0.01 0.75 0.667 0.706 0.90

13、9 tableware 0.793 0.016 0.885 0.793 0.836 0.89 headlampsWeighted Avg. 0.678 0.142 0.635 0.678 0.651 0.853= Confusion Matrix = a b c d e f g - classified as 59 10 1 0 0 0 0 | a = build wind float 20 52 1 0 3 0 0 | b = build wind non-float 12 5 0 0 0 0 0 | c = vehic wind float 0 0 0 0 0 0 0 | d = vehi

14、c wind non-float 0 5 0 0 5 0 3 | e = containers 0 2 0 0 1 6 0 | f = tableware 1 2 0 0 1 2 23 | g = headlampsK=7;= Summary =Correctly Classified Instances 137 64.0187 %Incorrectly Classified Instances 77 35.9813 %Kappa statistic 0.4948Mean absolute error 0.1147Root mean squared error 0.2557Relative a

15、bsolute error 54.1689 %Root relative squared error 78.7876 %Total Number of Instances 214 = Detailed Accuracy By Class = TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0.829 0.271 0.598 0.829 0.695 0.876 build wind float 0.605 0.181 0.648 0.605 0.626 0.852 build wind non-float 0.059 0.005

16、 0.5 0.059 0.105 0.71 vehic wind float 0 0 0 0 0 ? vehic wind non-float 0.308 0.03 0.4 0.308 0.348 0.939 containers 0.556 0.015 0.625 0.556 0.588 0.976 tableware 0.793 0.016 0.885 0.793 0.836 0.89 headlampsWeighted Avg. 0.64 0.158 0.636 0.64 0.617 0.864= Confusion Matrix = a b c d e f g - classified

17、 as 58 11 1 0 0 0 0 | a = build wind float 26 46 0 0 4 0 0 | b = build wind non-float 11 5 1 0 0 0 0 | c = vehic wind float 0 0 0 0 0 0 0 | d = vehic wind non-float 0 5 0 0 4 1 3 | e = containers 1 2 0 0 1 5 0 | f = tableware 1 2 0 0 1 2 23 | g = headlampsK=9;= Summary =Correctly Classified Instance

18、s 135 63.0841 %Incorrectly Classified Instances 79 36.9159 %Kappa statistic 0.4782Mean absolute error 0.1196Root mean squared error 0.2581Relative absolute error 56.4924 %Root relative squared error 79.5178 %Total Number of Instances 214 = Detailed Accuracy By Class = TP Rate FP Rate Precision Recal

19、l F-Measure ROC Area Class 0.829 0.278 0.592 0.829 0.69 0.881 build wind float 0.645 0.174 0.671 0.645 0.658 0.853 build wind non-float 0 0.005 0 0 0 0.694 vehic wind float 0 0 0 0 0 ? vehic wind non-float 0.231 0.03 0.333 0.231 0.273 0.933 containers 0.222 0.015 0.4 0.222 0.286 0.964 tableware 0.79

20、3 0.027 0.821 0.793 0.807 0.888 headlampsWeighted Avg. 0.631 0.159 0.58 0.631 0.597 0.864= Confusion Matrix = a b c d e f g - classified as 58 11 1 0 0 0 0 | a = build wind float 23 49 0 0 3 1 0 | b = build wind non-float 13 4 0 0 0 0 0 | c = vehic wind float 0 0 0 0 0 0 0 | d = vehic wind non-float

21、 0 6 0 0 3 0 4 | e = containers 3 1 0 0 2 2 1 | f = tableware 1 2 0 0 1 2 23 | g = headlampscontact-lenses:K=1;= Summary =Correctly Classified Instances 19 79.1667 %Incorrectly Classified Instances 5 20.8333 %Kappa statistic 0.6262Mean absolute error 0.2262Root mean squared error 0.3165Relative abso

22、lute error 59.8856 %Root relative squared error 72.4707 %Total Number of Instances 24 = Detailed Accuracy By Class = TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0.8 0.053 0.8 0.8 0.8 0.958 soft 0.75 0.1 0.6 0.75 0.667 0.925 hard 0.8 0.222 0.857 0.8 0.828 0.896 noneWeighted Avg. 0.792 0

23、.167 0.802 0.792 0.795 0.914= Confusion Matrix = a b c - classified as 4 0 1 | a = soft 0 3 1 | b = hard 1 2 12 | c = noneK=3;= Summary =Correctly Classified Instances 19 79.1667 %Incorrectly Classified Instances 5 20.8333 %Kappa statistic 0.6262Mean absolute error 0.2262Root mean squared error 0.31

24、65Relative absolute error 59.8856 %Root relative squared error 72.4707 %Total Number of Instances 24 = Detailed Accuracy By Class = TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0.8 0.053 0.8 0.8 0.8 0.958 soft 0.75 0.1 0.6 0.75 0.667 0.925 hard 0.8 0.222 0.857 0.8 0.828 0.896 noneWeight

25、ed Avg. 0.792 0.167 0.802 0.792 0.795 0.914= Confusion Matrix = a b c - classified as 4 0 1 | a = soft 0 3 1 | b = hard 1 2 12 | c = noneK=5;= Summary =Correctly Classified Instances 16 66.6667 %Incorrectly Classified Instances 8 33.3333 %Kappa statistic 0.3356Mean absolute error 0.2793Root mean squ

26、ared error 0.3624Relative absolute error 73.9227 %Root relative squared error 82.9705 %Total Number of Instances 24 = Detailed Accuracy By Class = TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0.6 0.053 0.75 0.6 0.667 0.947 soft 0.25 0.1 0.333 0.25 0.286 0.856 hard 0.8 0.556 0.706 0.8 0.

27、75 0.859 noneWeighted Avg. 0.667 0.375 0.653 0.667 0.655 0.877= Confusion Matrix = a b c - classified as 3 0 2 | a = soft 0 1 3 | b = hardK=7;= Summary =Correctly Classified Instances 14 58.3333 %Incorrectly Classified Instances 10 41.6667 %Kappa statistic -0.0619Mean absolute error 0.3188Root mean

28、squared error 0.387 Relative absolute error 84.3959 %Root relative squared error 88.61 %Total Number of Instances 24 = Detailed Accuracy By Class = TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0 0.053 0 0 0 0.947 soft 0 0 0 0 0 0.831 hard 0.933 1 0.609 0.933 0.737 0.807 noneWeighted Avg

29、. 0.583 0.636 0.38 0.583 0.461 0.841= Confusion Matrix = a b c - classified as 0 0 5 | a = soft 0 0 4 | b = hard 1 0 14 | c = noneK=9;= Summary =Correctly Classified Instances 14 58.3333 %Incorrectly Classified Instances 10 41.6667 %Kappa statistic -0.0619Mean absolute error 0.3188Root mean squared

30、error 0.387 Relative absolute error 84.3959 %Root relative squared error 88.61 %Total Number of Instances 24 = Detailed Accuracy By Class = TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0 0.053 0 0 0 0.947 soft 0 0 0 0 0 0.831 hard 0.933 1 0.609 0.933 0.737 0.807 noneWeighted Avg. 0.583 0.636 0.38 0.583 0.461 0.841= Confusion Matrix = a b c - classified as 0 0 5 | a = soft 0 0 4 | b = hard 1 0