Invoke the Weka packet for Kmeans Clustering (Java) __ Data mining

Data file used: 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 0.216000-0.608 000 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 0.2800 00 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 0.678000-0.49400 0 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 0.4  66000 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 0.3920 00 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 0.800000-0.38800 0 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 0.6  54000 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 0.366000-0.91
0000 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 0.848000-0.70600 0 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 0.7  30000 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 0.276000-0.88
2000 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 0.794000-0.288 000 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 0.7
 96000

Kmeans is a kind of very classical clustering algorithm. It uses the center of the cluster to the distance of the object to allocate the cluster affiliation of each object. At the same time, the update of the center of the cluster and the update of the cluster distribution, until convergence.

Here is the code that calls the Kmeans implemented in the Weka package

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 {
			//Read sample data
			file = new file ("Data/data1.txt");
			Arffloader loader = new Arffloader ();
			Loader.setfile (file);
			INS = Loader.getdataset ();
			
			Initialize the cluster (loading algorithm)
			KM = new Simplekmeans ();
			Km.setnumclusters (4); 		Set the number of categories to get the cluster
			km.buildclusterer (INS);		Start the cluster
			System.out.println (Km.preserveinstancesordertiptext ());
			Print clustering Results
			System.out.println (km.tostring ());
			
		} catch (Exception e) {
			e.printstacktrace ();
		}
	}
}