在掌握基于Weka工具的数据挖掘（分类、回归、聚类、关联规则分析）应用的基础上，实现基于Weka API的数据挖掘程序设计。
1.下载安装JDK 7.0 64位版，Weka 3.7版，Eclipse IDE for Java Developers 4.0以上版本。
2.基于Weka API的数据分类。
3.基于Weka API的数据回归。
4.基于Weka API的数据聚类。
5.基于Weka API的关联规则分析。
(1)JDK与Weka的安装方法与实验1中相同。
(2)从http://www.eclipse.org/home/index.php 下载并安装Eclipse。
(3)在Eclipse中建立一个新的Java工程，用于放置实验程序的源代码。
(4)编程请遵循Java编程规范。规范中文版参见：
http://www.hawstein.com/posts/google-java-style.html。
(a)将数值型字段规范化至[0,1]区间。
(b)调用特征选择算法（Select attributes），选择关键特征。
运行结果如下：
代码如下：
package weka;
import java.io.BufferedReader;
import java.io.FileReader;
import java.util.Random;
import weka.attributeSelection.AttributeSelection;
import weka.attributeSelection.BestFirst;
import weka.attributeSelection.CfsSubsetEval;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.evaluation.Evaluation;
import weka.classifiers.functions.MultilayerPerceptron;
import weka.classifiers.trees.J48;
import weka.classifiers.trees.RandomForest;
import weka.core.Attribute;
import weka.core.Instances;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Discretize;
import weka.filters.unsupervised.attribute.Normalize;
import weka.filters.unsupervised.attribute.NumericToNominal;
import weka.filters.unsupervised.attribute.Remove;
/**
* 二、基于Weka API的数据分类
* @author 西瓜不甜
*
*/
public class Test_1 {
public static void main(String[] args) throws Exception {
/**
* 1.读取"电费回收数据.csv"
*/
Instances data = new Instances(new BufferedReader(new FileReader("C:\\Users\\西瓜不甜\\Desktop\\workspace\\weka\\电费回收数据.arff")));
/**
* 2.数据预处理
* (1)删除无用属性
*/
String[] options = new String[2];
options[0] = "-R"; // remove
options[1] = "1-2,7,10,12"; // "CONS_NO", "YMD", "RCVED_DATE", "CUISHOU_COUNT", "YM"
Remove remove = new Remove();
remove.setOptions(options);
remove.setInputFormat(data);
data = Filter.useFilter(data, remove); // apply filter
data.setClassIndex(data.numAttributes() - 1); // set class
/**
* 2.数据预处理
* (2)归一化
*/
Normalize norm = new Normalize();
norm.setInputFormat(data);
data = Filter.useFilter(data, norm);
/**
* 2.数据预处理
* (3)特征选择
*/
AttributeSelection attSelect = new AttributeSelection();
CfsSubsetEval eval = new CfsSubsetEval();
BestFirst search = new BestFirst();
attSelect.setEvaluator(eval);
attSelect.setSearch(search);
attSelect.SelectAttributes(data);
int[] indices = attSelect.selectedAttributes();
System.out.print("\n Select Attributes : starting with (0): " + Utils.arrayToString(indices) + ‘\t‘);
for (int i = 0; i <indices.length; i++) {
int index = indices[i];
Attribute attribute = data.attribute(index);
System.out.print(attribute.name() + " ");
}
System.out.println();
/**
* 2.数据预处理
* (4) 离散化
*/
Discretize disc = new Discretize();
disc.setInputFormat(data);
data = Filter.useFilter(data, disc);
/**
* 2.数据预处理
* (5) 类型转换
*/
NumericToNominal nutono = new NumericToNominal();
nutono.setInputFormat(data);
data = Filter.useFilter(data, nutono);
/**
* 3.数据分类
* (0) 取60%为训练集，40%为测试集
*/
data.randomize(new Random(0));
int trainSize = (int) Math.round(data.numInstances() * 0.60);
int testSize = data.numInstances() - trainSize;
Instances train = new Instances(data, 0, trainSize);
Instances test = new Instances(data, trainSize, testSize);
/**
* 3.数据分类
* (1)决策树(J48)
*/
J48 j48 = new J48(); // train classifier
j48.buildClassifier(train);
Evaluation evaluation;
evaluation = new Evaluation(train); // evaluate classifier and print some statistics
evaluation.evaluateModel(j48, test);
System.out.println(evaluation.toSummaryString("\n======J48 : Results======\n", false));
System.out.println(evaluation.toClassDetailsString());
System.out.println(evaluation.toMatrixString());
/**
* 3.数据分类
* (2)随机森林(RandomForest)
*/
RandomForest rf = new RandomForest(); // train classifier
rf.buildClassifier(train);
evaluation = new Evaluation(train); // evaluate classifier and print some statistics
evaluation.evaluateModel(rf, test);
System.out.println(evaluation.toSummaryString("\n======RandomForest : Results======\n", false));
System.out.println(evaluation.toClassDetailsString());
System.out.println(evaluation.toMatrixString());
/**
* 3.数据分类
* (3)神经网络(MultilayerPerceptron)
* ps:运行时间约4h,谨慎运行。
*/
MultilayerPerceptron mp = new MultilayerPerceptron(); // train classifier
mp.buildClassifier(train);
evaluation = new Evaluation(train); // evaluate classifier and print some statistics
evaluation.evaluateModel(mp, test);
System.out.println(evaluation.toSummaryString("\n======MultilayerPerceptron : Results======\n", false));
System.out.println(evaluation.toClassDetailsString());
System.out.println(evaluation.toMatrixString());
/**
* 3.数据分类
* (4)朴素贝叶斯(NaiveBayes)
*/
NaiveBayes nb = new NaiveBayes(); // train classifier
nb.buildClassifier(train);
evaluation = new Evaluation(train); // evaluate classifier and print some statistics
evaluation.evaluateModel(nb, test);
System.out.println(evaluation.toSummaryString("\n======NaiveBayes : Results======\n", false));
System.out.println(evaluation.toClassDetailsString());
System.out.println(evaluation.toMatrixString());
}
}
(a)将数值型字段规范化至[0,1]区间。
(b)调用特征选择算法（Select attributes），选择关键特征。
运行结果如下：
原文：https://www.cnblogs.com/xgbt/p/13334026.html