西电数据挖掘决策树算法

1、数据挖掘算法实验报告1) 实验题目基于决策树的分类算法，属性的选择采用ID3或C4.5策略，采用如下的数 据建立分类决策树。ageincomestudentcredit ratingbuys computer=30highnofairno40mediumnofairves40lowyesrairyes4Qlowvesexcellentno31. .40lowvesexcellentves=30mediumnofairno40mediumyesfairyes40mediumnoexcelentno2)算法基本思想的描述ID3选择具有最高信息熵增益的属性作为分裂属性，基于这种原则我们 首先可以算出

2、初始集合的熵，然后分别求出以各个属性为分裂属性时的熵， 然后将通过上面得到的数据算出以各个属性为分裂属性时的信心增益，选择 具有最大的信息增益属性作为我们的分裂属性。3) 编程实现算法#include #include #include using namespace std;#define SIZE 14struct Data(char age10;char income10;char student10;char credit_rating20;char buys_computer10;；Data data SIZE=(=30，high，气o，fair，气o,40,medium,no,fa

3、ir,yes,40,low,yes,fair,yes,40,low,yes,excellent,no,31.40,low,yes,excellent,yes,=30,medium,no,fair,no,40,medium,yes,fair,yes,40,medium,no,excellent,no；double calculate(double a,double b);void origin_entropy(Data data,double &entropy);void age_entropy(Data data,double &entropy);void income_entropy(Dat

4、a data,double &entropy);void student_entropy(Data data,double &entropy);void credit_rating_entropy(Data data,double &entropy);int main()(double origin=0,age=0,student=0,credit_rating=0,income=0;origin_entropy(data,origin);age_entropy(data,age);student_entropy(data,student);income_entropy(data,income

5、);credit_rating_entropy(data,credit_rating);coutinfo(D)=originendl;cout用 age 作为分裂属性时：侦熵 info(age)(D)=agett信息增益为：origin-ageendl;cout用 income 作为分裂属性时：n熵 info(income)(D)=incomett 信 息 增 益 为：origin-incomeendl;cout用 student 作为分裂属性时：n熵 info(student)(D)=):studenttt 信 息 增 益 为：origin-studentendl;cout用 credit_

6、rating 作为分裂属性时：n 熵 info(credit_rating)(D)=credit_ratingt 信息增益 为：origin-credit_ratingendl;return 0;double calculate(double a,double b)(if(a=0)return 0;elsereturn (a/b)*log10(b/a)/log10(2);void origin_entropy(Data data,double &entropy)(int i;double yes=0, no=0;for( i=0;iSIZE;i+)(if(strcmp(datai.buys_c

7、omputer,yes)=0)yes+;elseno+;entropy二calculate(yes,SIZE)+calculate(no,SIZE);void age_entropy(Data data,double &entropy)(double youth3=0;double middle3=0;double old3 = 0;for (int i=0;iSIZE;i+)(if(strcmp(datai.age,=30)=0)(strcmp(datai.buys_computer,yes)=0)?youth0+:youth1+ ;youth2+;else if(strcmp(datai.

8、age,31.40)=0)(strcmp(datai.buys_computer,yes)=0)?middle0+:middle1 +;middle2+;else(strcmp(datai.buys_computer,yes)=0)?old0+:old1+;old 2+;entropy二youth2/SIZE*(calculate(youth0,youth2)+calculate( youth1,youth2)+middle2/SIZE*(calculate(middle0,middle2 )+calculate(middle1,middle2)+old2/SIZE*(calculate(ol

9、d0 ,old2)+calculate(old1,old2);void income_entropy(Data data,double &entropy)(double high3 = 0;double medium3=0;double low3 = 0;for (int i=0;iSIZE;i+)(if(strcmp(datai.income,high)=0)(strcmp(datai.buys_computer,yes)=0)?high0+:high1+;h igh2+;else if(strcmp(datai.income,medium)=0)(strcmp(datai.buys_com

10、puter,yes)=0)?medium0+:medium1 +;medium2+;else(strcmp(datai.buys_computer,yes)=0)?low0+:low1+;low 2+;entropy=high2/SIZE*(calculate(high0,high2)+calculate(hig h1,high2)+medium2/SIZE*(calculate(medium0,medium2)+ca lculate(medium1,medium2)+low2/SIZE*(calculate(low0,low 2)+calculate(low1,low2);void stud

11、ent_entropy(Data data,double &entropy)(double yes3 = 0;double no3 = 0;for (int i=0;iSIZE;i+)(if(strcmp(datai.student,yes)(strcmp(datai.buys_computer,yes)？yes0+:yes1+;yes2+7else(strcmp(datai.buys_computer,yes)？no0+:no1+;no2+;entropy=yes2/SIZE*(calculate(yes0,yes2)+calculate(yes1,yes2)+no2/SIZE*(calculate(no0,no2)+calculate(no1,no 2);void credit_rating_entropy(Data data,double &entropy)(double fair3 = 0;double excellent3=0;for (int i=0;iSIZE;i+)(if(strcmp(datai.credit_rating,fair)(strcmp(datai.buys_computer,yes)?fair0+:fair1+;fair2+;else(strcmp(datai.buys_computer,yes)?excellent0+:exce