weka:调用内置算法挖掘数据关联规则

创建一个Java Project，使用weka中自带的数据集weather.nomial.arff和weather.number.arff，调用weka中的apriori算法，以及FPGrowth算法分别进行挖掘关联规则。

 

public class test3 {

/**
* @param args
* @throws Exception 
*/
public static void main(String[] args) throws Exception {

/*处理weather.nominal.arff
* */
Instances instances = DataSource.read("C:\\Program Files (x86)\\Weka-3-5\\data\\weather.nominal.arff");
instances.setClassIndex(instances.numAttributes() - 1); 
/*创建Apriori实例
* */
Apriori apriori = new Apriori(); 
apriori.buildAssociations( instances ); 
System.out.println(apriori.toString());

               /*处理weather.number.arff

* */
Instances test=DataSource.read("C:\\Program Files (x86)\\Weka-3-5\\data\\weather.arff");
//获取对象的数据
          test.setClassIndex(test.numAttributes() - 1); 
          //创建一个离散实例
Discretize discretize = new Discretize();
              /*离散化处理
               * */

String[] options = new String[6]; 
            options[0] = "-B"; options[1] = "8"; options[2] = "-M"; options[3] = "-1.0";options[4] = "-R";options[5] = "2-last"; 
               discretize.setOptions(options); 
discretize.setInputFormat(test); 
/*获取离散化处理后的数据对象
* */
Instances newInstances2 = Filter.useFilter(test, discretize); 
newInstances2.setClassIndex(newInstances2.numAttributes() - 1); 
/*创建Apriori实例
* */
Apriori apriori2 = new Apriori(); 
apriori2.buildAssociations(newInstances2 ); 
System.out.println(apriori2.toString());



}


}