public class demo { public demo() throws Exception { // TODO Auto-generated constructor stub BufferedReader breader = null; breader = new BufferedReader(new FileReader( "D:/logiciels/weka-3-7-12/weka-3-7-12/data/iris.arff")); Instances Train = new Instances(breader); //Train.setClassIndex(Train.numAttributes() - 1); // comment out this line SimpleKMeans kMeans = new SimpleKMeans(); kMeans.setSeed(10); kMeans.setPreserveInstancesOrder(true); kMeans.setNumClusters(3); kMeans.buildClusterer(Train); int[] assignments = kMeans.getAssignments(); int i = 0; for (int clusterNum : assignments) { System.out.printf("Instance %d -> Cluster %d", i, clusterNum); i++; } breader.close(); } public static void main(String[] args) throws Exception { // TODO Auto-generated method stub new demo(); } }
clusterer.buildClusterer(datasets[i]); double SSE = clusterer.getSquaredError(); if (SSE < minSSE) {
clusterer.setFastDistanceCalc(true); clusterer.buildClusterer(data);
Instances instances = new Instances("iris.arff"); SimpleKMeans simpleKMeans = new SimpleKMeans(); // build clusterer simpleKMeans.setPreservationOrder(true); simpleKMeans.setNumClusters(2); simpleKMeans.buildClusterer(instances); ClusterEvaluation eval = new ClusterEvaluation(); eval.setClusterer(simpleKMeans); eval.evaluateClusterer(instances); System.out.println("Cluster Evaluation: "+eval.clusterResultsToString());
private void trainModel(Map<Long, Double> metricData) throws Exception { //Model has a single metric_value attribute Attribute value = new Attribute("metric_value"); FastVector attributes = new FastVector(); attributes.addElement(value); trainingData = new Instances("metric_value_data", attributes, 0); for (Double val : metricData.values()) { double[] valArray = new double[] { val }; Instance instance = new Instance(1.0, valArray); trainingData.add(instance); } //Create and train the model model = new SimpleKMeans(); model.setNumClusters(k); model.setMaxIterations(20); model.setPreserveInstancesOrder(true); model.buildClusterer(trainingData); clusterCentroids = model.getClusterCentroids(); centroidAssignments = model.getAssignments(); setMeanDistancesToCentroids(); }
private void trainModel(Map<Long, Double> metricData) throws Exception { //Model has a single metric_value attribute Attribute value = new Attribute("metric_value"); FastVector attributes = new FastVector(); attributes.addElement(value); trainingData = new Instances("metric_value_data", attributes, 0); for (Double val : metricData.values()) { double[] valArray = new double[] { val }; Instance instance = new Instance(1.0, valArray); trainingData.add(instance); } //Create and train the model model = new SimpleKMeans(); model.setNumClusters(k); model.setMaxIterations(20); model.setPreserveInstancesOrder(true); model.buildClusterer(trainingData); clusterCentroids = model.getClusterCentroids(); centroidAssignments = model.getAssignments(); setMeanDistancesToCentroids(); }
localKMeans.setInitializationMethod(new SelectedTag( SimpleKMeans.KMEANS_PLUS_PLUS, SimpleKMeans.TAGS_SELECTION)); localKMeans.buildClusterer(sketchForRun); finalStartPointsForRuns.add(localKMeans.getClusterCentroids()); } catch (Exception ex) {
sk.setDoNotCheckCapabilities(true); sk.setDontReplaceMissingValues(true); sk.buildClusterer(inst); if (sk.getSquaredError() < bestSqE) { bestSqE = sk.getSquaredError();
sk.setDoNotCheckCapabilities(true); sk.setDontReplaceMissingValues(true); sk.buildClusterer(inst); if (sk.getSquaredError() < bestSqE) { bestSqE = sk.getSquaredError();