weka是机器学习和数据挖掘库java库，提供有GUI界面版本和类库版本。weka是GNU通用公共许可证下发布的开源软件，这意味着我们在自己的项目自由的使用，并可以在开放源代码的情况下改进weka。在使用weka之前，我们首先要将自己的数据集转换为weka支持的格式。weka的数据格式请参考arff格式说明。学习weka库最好先有一下机器学习方面的基础知识，然后学习官方example。接下来简单说明如何使用weka库的kmeans算法。
训练数据集集
@relation race
@attribute height NUMERIC
@attribute weight NUMERIC
@attribute eyeColor NUMERIC
@attribute hairColor NUMERIC
@data
170,70,1,1
180,70,2,2
175,70,1,1
200,80,2,3
210,80,2,1
200,70,2,2
150,50,1,1
155,50,1,1
170,70,1,1
173,70,1,1
170,70,1,2
测试数据集集
@relation race
@attribute height NUMERIC
@attribute weight NUMERIC
@attribute eyeColor NUMERIC
@attribute hairColor NUMERIC
@data
170,71,1,1
200,80,2,2
1.代码
public class KmeansTest {
public static void main(String[] args) throws Exception {
File trainFile = new File("race_train.arff");
File testFile = new File("race_test.arff");
ArffLoader atf = new ArffLoader();
// load data
atf.setFile(trainFile);
Instances train = atf.getDataSet();
atf.setFile(testFile);
Instances test = atf.getDataSet();
// build clusterer
SimpleKMeans clusterer = new SimpleKMeans();
clusterer.setNumClusters(2);
clusterer.buildClusterer(train);
System.out.println(clusterer.toString());
// output predictions
for (int i = 0; i < test.numInstances(); i++) {
int cluster = clusterer.clusterInstance(test.instance(i));
double[] dist = clusterer.distributionForInstance(test.instance(i));
System.out.print((i+1));
System.out.print(" - ");
System.out.print(cluster);
System.out.print(" - ");
System.out.print(Utils.arrayToString(dist));
System.out.println();
}
}
}
2.结果如下
//kMeans聚类结果信息如下，比较重要的是每个聚类的中心以及误差
kMeans
======
Number of iterations: 2
Within cluster sum of squared errors: 1.7463888888888888
Missing values globally replaced with mean/mode
Cluster centroids:
Cluster#
Attribute Full Data 0 1
(11) (4) (7)
============================================
height 177.5455 197.5 166.1429
weight 68.1818 75 64.2857
eyeColor 1.3636 2 1
hairColor 1.4545 2 1.1429
//分类结果如下
1 - 1 - 0.0,1.0
2 - 0 - 1.0,0.0
更多资料请阅读weka官网。