1、數據挖掘實驗報告實驗序號：實驗項目名稱：C4.5算法學號姓名專業、班12數學金融實驗地點實驗樓5-510指導教師潘巍巍實驗時間2014.12.24一、實驗目的及要求1:選擇一個數據挖掘標準數據集，采用C4.5算法進行分類，給出分類精度，畫出用C4.5算法誘導的樹并寫出生成的規則集合。2:在數據挖掘標準數據集上，實驗對比剪枝與未剪枝的樹的分類性能。3:總結C4.5算法的優缺點二、實驗設備（環境）及要求電腦WEKA 3.6.1三、實驗內容與步驟（3）數據分類（c4.5算法實現）1.導入數據丹1蘆価=AjHGjii-tfiy百irgir西Crast1y5243TtiTQT亍DLbULJ-Ct 1ff

2、nsul% 葉0 tt)Slftvor: -stillsHEHiHtlc 0帕llirrBLt Fdlflt3KnjLcati E va-kduz EjiTiboliEIn&iafictfr 14iLttribales 5SfilFet* 3.itFlbE0l*(2)選擇C4.5分類器進行分類結果為fQ Weka Explorer1=1 |1Pri-proccKS -laESi fy Cluster JkssaciH-teitiributx Vi snislizie-Status_OK “IEx 0其中分類精度為50%生成的決策樹為outook=a uh nr - CNe rcasf帕

3、勲 4.即也 （3 0）一TRUEoutlook = sunny| humidity = high: no (3.0)| humidity = normal: yes (2.0)outlook = overcast: yes (4.0) outlook= rainy| win dy = TRUE: no (2.0)| wi ndy = FALSE: yes (3.0)剪枝后結果為分類規則：J48 pruned treenumMit/匕nigh - rormarTijjj WfScfiC 占網ifr r呼PW中-汗脫的1十尸“TI)a分類精度變為57.1%性能變好(1)C4.5算法優缺點優點：分

4、類精度高，生成的分類規則比較簡單，易于理解。缺點：需要多次掃描數據集，比較低效五、分析與討論六、教師評語QWeks Explof-er| n | | S3Pr -pr-*riiBKClfiSSl Ers a i： k s ta.1 tp VBII&BIIEXBStitLii成績簽名:日期:數據挖掘實驗報告實驗序號：實驗項目名稱：KNh算法、實驗目的及要求KNN算法的基本思路、步驟。選擇UCI中的5個標準數據集，使用KNN算法在該數據集上計算混淆矩陣。選擇2個數據集，選擇不同的k值，k=1,3,5,7,9，對比KNN算法計算結果的差異。、實驗設備（環境）及要求電腦WEKA 3.6.1四、

5、實驗內容與步驟1.數據集contact-lenses.arffGlass.arff兩者的混淆矩陣分別為=Confusion Mate!暮=a b c - cla-ssified耳孕2121 a - scsfi:1 2 1 I b = hard專業、班12數學金融實驗地點實驗樓5-510指導教師潘巍巍實驗時間2014.12.2440 11 I nonef = tableware g = headidwaEL5Sbgcd0e0f0g01S 5140321$a5Q000000Q0000200id0101001700亍0021冇classified asEL buildwindfloatb build

6、windnan-floatc =vetiicwindSloatd =vehlcwindnan-floateMatrix(2)兩個數據集在K=1,3,5,7,9下結果分別為Glass：K=1;=Summary =Correctly Classified In sta nces151In correctly Classified In sta nces63Kappa statistic0.6005Mea n absolute error0.0897Root mean squared error0.2852Relative absolute error42.3747 %Root relative s

7、quared error87.8627 %Total Number of In sta nces214=Detailed Accuracy By Class =TP RateFP RatePrecisi onRecallF-MeasureROC AreaClass0.7860.1670.6960.7860.7380.806build wind float0.6710.130.7390.6710.7030.765build wi ndnon-float0.2940.0510.3330.2940.3130.59vehic wind float00000?vehic wi ndnon-float0.

8、7690.030.6250.7690.690.895containers0.7780.0150.70.7780.7370.838tableware0.7930.0110.920.7930.8520.884headlampsWeighted Avg.0.7060.1090.7090.7060.7040.792=Con fusi on Matrix =abcdefg-classified as55960000 1a=build wind float15 5140321 1b =build wind non-float9350000 1c :=vehic wind float0000000 1d=v

9、ehic wind non-float0200 1001 |e = =containers0100170 1f =:tableware030021 23 |g =headlampsK=3;=Summary =70.5607 %29.4393 %Correctly Classified In sta nces Incorrectly Classified In sta nces Kappastatistic154600.609771.9626 %28.0374 %Mea n absolute error0.0983Root mean squared error0.2524Relative abs

10、olute error46.4438 %Root relative squared error77.7792 %Total Number of In sta nces214=Detailed Accuracy By Class =TP RateFP RatePrecisi onRecallF-MeasureROC AreaClass0.8430.2150.6560.8430.7380.865buildwind float0.7110.1380.740.7110.7250.835buildwind non-float0.1760.0150.50.1760.2610.672vehic windfl

11、oat00000?vehicwind non-float0.6150.0150.7270.6150.6670.913containers0.7780.010.7780.7780.7780.914tableware0.7930.0110.920.7930.8520.885headlampsWeighted Avg.0.720.1230.7180.720.7080.847=Con fusi on Matrix =abcdefg-classified as59 1010000 1a : =build wind float19 5420100 1b : =build wind non-float104

12、30000 1c=vehic wind float0000000 1d : =vehic wind non-float0300802 |e : =containers0100170 1f =:tableware210012 23 |g :=headlampsK=5;=Summary =Correctly Classified In sta nces14567.757 %In correctly Classified In sta nces6932.243 %Kappa statistic0.5469Mea n absolute error0.1085Root mean squared erro

13、r0.2563Relative absolute error51.243 %Root relative squared error78.9576 %Total Number of In sta nces214=Detailed Accuracy By Class =TP Rate FP Rate Precisi on Recall F-Measure ROC Area Class0.8430.2290.6410.8430.7280.867build wind float0.6840.174build wind non-float0.6840.6840.6840.84800.01 0000.64

14、2vehic wind float00 000?vehic wind non-float0.3850.0250.50.3850.4350.952containers0.6670.010.750.6670.7060.909tableware0.7930.0160.8850.7930.8360.89headlampsWeighted Avg. 0.6780.1420.6350.6780.6510.853=Con fusi on Matrix =a b c d e f g - classified as59 10 1 0 0 0 0 | a = build wi nd float20 52 1 0

15、3 0 0 | b = build wind non-float12 5 0 0 0 0 0 | c = vehic wind float0 0 0 0 0 0 0 | d = vehic wi nd non-float0 5 0 0 5 0 3 | e = con tai ners0 2 0 0 1 6 0 | f = tableware1 2 0 0 1 2 23 | g = headlampsK=7;= Summary =Correctly Classified In sta nces13764.0187 %In correctly Classified In sta nces7735.

16、9813 %Kappa statistic0.4948Mean absolute error0.1147Root mean squared error0.2557Relative absolute error54.1689%Root relative squared error78.7876 %Total Number of In sta nces214=Detailed Accuracy By Class =TP Rate FP Rate Precisi on Recall F-Measure ROC Area Class0.8290.2710.5980.8290.6950.876build

17、 wind float0.6050.181build wind non-float0.6480.6050.6260.8520.0590.0050.50.0590.1050.71vehic wind float00 000?vehic wind non-float0.3080.030.40.3080.3480.939containers0.5560.0150.6250.5560.5880.976tableware0.7930.0160.8850.7930.8360.89headlampsWeighted Avg. 0.640.1580.6360.640.6170.864=Con fusi on

18、Matrix =a b c d e f g - classified as58 11 1 0 0 0 0 | a = build wi nd float26 46 0 0 4 0 0 | b = build wind non-float11 5 1 0 0 0 0 | c = vehic wind float0 0 0 0 0 0 0 | d = vehic wi nd non-float0 5 0 0 4 1 3 | e = con tai ners1 2 0 0 1 5 0 | f = tableware1 2 0 0 1 2 23 | g = headlampsK=9;=Summary

19、=Correctly Classified In sta nces Incorrectly Classified In sta nces Kappastatistic(Mean absolute error Root meansquared error Relative absolute errorRoot relative squared error TotalNumber of In sta nces =Detailed Accuracy By Class =a b c d e f g - classified as58 11 1 0 0 0 0 | a = build wi nd flo

20、at0.8290.2780.5920.8290.690.881build wind float0.6450.1740.6710.6450.6580.853build wind non-float00.0050 000.694vehic wind float00 (0 00?vehic wind non-float0.2310.030.3330.2310.2730.933containers0.2220.0150.40.2220.2860.964tableware0.7930.0270.8210.7930.8070.888headlampsWeighted Avg.0.6310.1590.580

21、.6310.5970.864=Con fusi on Matrix =63.0841 %36.9159 %135.790.47820.11960.258156.4924 %79.5178 %TP RateClassFP RatePrecisi on RecallF-Measure ROC Area23 49 0 0 3 10| b=build wind non-float13 4 0 0 0 00 |c : =vehic wind float0 0 0 0 0 00 |d =:vehic wind non-float0 6 0 0 3 04 |e = :containers3 1 0 0 2

22、21 |f = tableware1 2 0 0 1 2 23 |g : =headlampscon tact-le nses:K=1;=Summary =Con fusi on Matrix =-classified as a = soft b = hard2 12 |c = noneCorrectly Classified In sta nces1979.1667 %In correctly Classified In sta nces20.8333 %Kappa statistic0.6262Mea n absolute error0.2262Root mea n squared err

23、or0.3165Relative absolute error59.8856 %Root relative squared error72.4707 %Total Number of In sta nces24=Detailed Accuracy By Class =Weighted Avg.TP RateFP RatePrecisi onRecallF-MeasureROC AreaClass0.80.0530.80.80.80.958soft0.750.10.60.750.6670.925hard0.80.2220.8570.80.8280.896none0.7920.1670.8020.

24、7920.7950.914K=3;=Summary =Correctly Classified In sta nces Incorrectly Classified In sta nces Kappastatistic(Mean absolute error Root meansquared error Relative absolute errorRoot relative squared error TotalNumber of In sta nces19.50.62620.22620.316559.8856 %72.4707 %2479.1667 %20.8333 %=Detailed

25、Accuracy By Class =Recall F-Measure ROC AreaClass0.80.0530.80.80.80.958soft0.750.10.60.750.6670.925hard0.80.2220.8570.80.8280.896noneWeighted Avg.0.7920.1670.8020.7920.795TP Rate FP Rate Precision0.914=Con fusi on Matrix =a401b c - classified as0 1 | a = soft3 1 | b = hard212 | c = noneK=5;=Summary

26、=Correctly Classified In sta nces Incorrectly Classified In sta nces Kappastatistic(Mean absolute errorRoot mea n squared error16. 80.33560.27930.362466.6667 %33.3333 %Relative absolute error73.9227 %Root relative squared error82.9705 %Total Number of In sta nces24=Detailed Accuracy By Class=TP Rate

27、 FP Rate Precisio n Recall F-MeasureROC AreaClass0.60.0530.750.60.6670.947soft0.250.10.3330.250.2860.856hard0.80.5560.7060.80.750.859noneWeighted Avg. 0.6670.3750.6530.6670.6550.877=Con fusi on Matrix =a b c - classified as3 0 2 | a = soft0 1 3 | b = hardK=7;= Summary =Correctly Classified In sta nces1458.3333 %In correctly Classified In sta nces1041.6667 %Kappa statistic-0.0619Mean absolute error0.3188Root mean squared error0.387Relative absolute error84.3959 %Root relative squared error88