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  • 调用WEKA包进行kmeans聚类(java)

    所用数据文件:data1.txt

    @RELATION data1
    
    
    @ATTRIBUTE one REAL
    @ATTRIBUTE two REAL
    
    
    
    
    @DATA
    0.184000 0.482000
    0.152000 0.540000
    0.152000 0.596000
    0.178000 0.626000
    0.206000 0.598000
    0.230000 0.562000
    0.224000 0.524000
    0.204000 0.540000
    0.190000 0.572000
    0.216000 0.608000
    0.240000 0.626000
    0.256000 0.584000
    0.272000 0.546000
    0.234000 0.468000
    0.222000 0.490000
    0.214000 0.414000
    0.252000 0.336000
    0.298000 0.336000
    0.316000 0.376000
    0.318000 0.434000
    0.308000 0.480000
    0.272000 0.408000
    0.272000 0.462000
    0.280000 0.524000
    0.296000 0.544000
    0.340000 0.534000
    0.346000 0.422000
    0.354000 0.356000
    0.160000 0.282000
    0.160000 0.282000
    0.156000 0.398000
    0.138000 0.466000
    0.154000 0.442000
    0.180000 0.334000
    0.184000 0.300000
    0.684000 0.420000
    0.678000 0.494000
    0.710000 0.592000
    0.716000 0.508000
    0.744000 0.528000
    0.716000 0.540000
    0.692000 0.540000
    0.696000 0.494000
    0.722000 0.466000
    0.738000 0.474000
    0.746000 0.484000
    0.750000 0.500000
    0.746000 0.440000
    0.718000 0.446000
    0.692000 0.466000
    0.746000 0.418000
    0.768000 0.460000
    0.272000 0.290000
    0.240000 0.376000
    0.212000 0.410000
    0.154000 0.564000
    0.252000 0.704000
    0.298000 0.714000
    0.314000 0.668000
    0.326000 0.566000
    0.344000 0.468000
    0.324000 0.632000
    0.164000 0.688000
    0.216000 0.684000
    0.392000 0.682000
    0.392000 0.628000
    0.392000 0.518000
    0.398000 0.502000
    0.392000 0.364000
    0.360000 0.308000
    0.326000 0.308000
    0.402000 0.342000
    0.404000 0.418000
    0.634000 0.458000
    0.650000 0.378000
    0.698000 0.348000
    0.732000 0.350000
    0.766000 0.364000
    0.800000 0.388000
    0.808000 0.428000
    0.826000 0.466000
    0.842000 0.510000
    0.842000 0.556000
    0.830000 0.594000
    0.772000 0.646000
    0.708000 0.654000
    0.632000 0.640000
    0.628000 0.564000
    0.624000 0.352000
    0.650000 0.286000
    0.694000 0.242000
    0.732000 0.214000
    0.832000 0.214000
    0.832000 0.264000
    0.796000 0.280000
    0.778000 0.288000
    0.770000 0.294000
    0.892000 0.342000
    0.910000 0.366000
    0.910000 0.394000
    0.872000 0.382000
    0.774000 0.314000
    0.718000 0.252000
    0.688000 0.284000
    0.648000 0.322000
    0.602000 0.460000
    0.596000 0.496000
    0.570000 0.550000
    0.564000 0.592000
    0.574000 0.624000
    0.582000 0.644000
    0.596000 0.664000
    0.662000 0.704000
    0.692000 0.722000
    0.710000 0.736000
    0.848000 0.732000
    0.888000 0.686000
    0.924000 0.514000
    0.914000 0.470000
    0.880000 0.492000
    0.848000 0.706000
    0.730000 0.736000
    0.676000 0.734000
    0.628000 0.732000
    0.782000 0.708000
    0.806000 0.674000
    0.830000 0.630000
    0.564000 0.730000
    0.554000 0.538000
    0.570000 0.502000
    0.572000 0.432000
    0.590000 0.356000
    0.652000 0.232000
    0.676000 0.178000
    0.684000 0.152000
    0.728000 0.172000
    0.758000 0.148000
    0.864000 0.176000
    0.646000 0.242000
    0.638000 0.254000
    0.766000 0.276000
    0.882000 0.278000
    0.900000 0.278000
    0.906000 0.302000
    0.892000 0.316000
    0.570000 0.324000
    0.798000 0.150000
    0.832000 0.114000
    0.714000 0.156000
    0.648000 0.154000
    0.644000 0.212000
    0.642000 0.250000
    0.658000 0.284000
    0.710000 0.296000
    0.794000 0.288000
    0.846000 0.260000
    0.856000 0.304000
    0.858000 0.392000
    0.858000 0.476000
    0.778000 0.640000
    0.736000 0.662000
    0.718000 0.690000
    0.634000 0.692000
    0.596000 0.710000
    0.570000 0.720000
    0.554000 0.732000
    0.548000 0.686000
    0.524000 0.740000
    0.598000 0.768000
    0.660000 0.796000
    

    前言:Kmeans是一种非常经典的聚类算法。它利用簇的中心到对象的距离来分配每个对象的簇所属关系。同时迭代的进行簇的中心的更新以及簇分配的更新,直到收敛。


    下面是调用weka包中实现的kmeans的代码


    package others;
    
    import java.io.File;
    
    import weka.clusterers.SimpleKMeans;
    import weka.core.DistanceFunction;
    import weka.core.Instances;
    import weka.core.converters.ArffLoader;
    
    public class ArrayListTest {
    
    	public static void main(String[] args){
    		Instances ins = null;
    		
    		SimpleKMeans KM = null;
    		DistanceFunction disFun = null;
    		
    		try {
    			// 读入样本数据
    			File file = new File("data/data1.txt");
    			ArffLoader loader = new ArffLoader();
    			loader.setFile(file);
    			ins = loader.getDataSet();
    			
    			// 初始化聚类器 (加载算法)
    			KM = new SimpleKMeans();
    			KM.setNumClusters(4); 		//设置聚类要得到的类别数量
    			KM.buildClusterer(ins);		//开始进行聚类
    			System.out.println(KM.preserveInstancesOrderTipText());
    			// 打印聚类结果
    			System.out.println(KM.toString());
    			
    		} catch(Exception e) {
    			e.printStackTrace();
    		}
    	}
    }




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  • 原文地址:https://www.cnblogs.com/wenbaoli/p/5655747.html
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