List of usage examples for weka.clusterers SimpleKMeans SimpleKMeans
public SimpleKMeans()
From source file:com.spread.experiment.tempuntilofficialrelease.ClassificationViaClustering108.java
License:Open Source License
/** * default constructor */ public ClassificationViaClustering108() { super(); m_Clusterer = new SimpleKMeans(); }
From source file:controller.KMeansBean.java
public void calculate() { SimpleKMeans skm = new SimpleKMeans(); try {/*w w w.j av a 2 s . c o m*/ skm.setNumClusters(clusternum); skm.buildClusterer(inst); output = skm.toString(); } catch (Exception ex) { Logger.getLogger(KMeansBean.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:controller.MineroControler.java
public String clasificarSimpleKmeans(int numClusters) { BufferedReader breader = null; Instances datos = null;//from www . j a v a 2s .co m breader = new BufferedReader(fuente_arff); try { datos = new Instances(breader); } catch (IOException ex) { System.err.println("Problemas al intentar cargar los datos"); } SimpleKMeans skmeans = new SimpleKMeans(); try { skmeans.setSeed(10); skmeans.setPreserveInstancesOrder(true); skmeans.setNumClusters(numClusters); skmeans.buildClusterer(datos); } catch (Exception ex) { System.err.println("Problemas al ejecutar algorimo de clasificacion"); } return skmeans.toString(); }
From source file:core.ClusterEvaluationEX.java
License:Open Source License
/** * Constructor. Sets defaults for each member variable. Default Clusterer * is EM./*from ww w . j av a2s . c o m*/ */ public ClusterEvaluationEX() { setClusterer(new SimpleKMeans()); m_clusteringResults = new StringBuffer(); m_clusterAssignments = null; }
From source file:dataHandlers.DataClusterHandler.java
private void initiateClusters(int randomSeedMax, int clusterCount) { if (!clusterInitiated) { dataGraph = new SimpleKMeans(); dataGraph.setPreserveInstancesOrder(true); dataGraph.setSeed(randomSeedMax); LOGGER.log(Level.WARNING, "May throw exception if the cluster count is a negative value"); try {/*from ww w. j ava 2 s .c o m*/ dataGraph.setNumClusters(clusterCount); } catch (Exception e) { LOGGER.log(Level.SEVERE, "Error@ClusterProcess_initiateCluster", e); clusterCount = ((-1) * clusterCount) + 1; initiateClusters(randomSeedMax, clusterCount); } clusterInitiated = true; } }
From source file:de.unimannheim.dws.algorithms.CustomSimpleKMedian.java
License:Open Source License
/** * Main method for testing this class./* www. jav a 2s. com*/ * * @param argv should contain the following arguments: * <p> * -t training file [-N number of clusters] */ public static void main(String[] argv) { runClusterer(new SimpleKMeans(), argv); }
From source file:detplagiasi.KMeansClustering.java
KMeansClustering() { addd = Container.getAddress(); try {/*w ww .j a va 2 s.c o m*/ ClusterEvaluation eval; Instances data; String[] options; SimpleKMeans cl; File he = getArffFile(); data = new Instances(new BufferedReader(new FileReader(he))); System.out.println("-----KMeans Clustering-----"); // normal try (BufferedWriter out = new BufferedWriter(new FileWriter(addd + "\\output.txt", true))) { out.write("\r\n--> normal\r\n"); options = new String[2]; options[0] = "-t"; options[1] = he.getAbsolutePath(); out.write("\r\n" + ClusterEvaluation.evaluateClusterer(new SimpleKMeans(), options) + "\r\n"); out.write("\r\n"); // manual call out.write("\n--> manual\r\n"); cl = new SimpleKMeans(); cl.setNumClusters(4); out.write("\r\n"); cl.buildClusterer(data); getDataUji(); System.out.println("jumlah kluster = " + cl.numberOfClusters()); System.out.println("kluster = " + cl.clusterInstance(dataUji.instance(0))); noClusterUji = cl.clusterInstance(dataUji.instance(0)); totalCluster = cl.numberOfClusters(); for (int b = 0; b < dataTraining.numInstances(); b++) { System.out.print("file " + td.fileName[b] + " termasuk cluster ke "); System.out.println(cl.clusterInstance(dataTraining.instance(b))); array1[b] = td.fileName[b]; array2[b] = cl.clusterInstance(dataTraining.instance(b)); //simpan nilai instance ke dalam sebuah array int buat dikirim ke detplaggui } out.write("\r\n"); eval = new ClusterEvaluation(); eval.setClusterer(cl); eval.evaluateClusterer(new Instances(data)); out.write("\r\n\n# of clusters: " + eval.getNumClusters()); } catch (Exception e) { System.err.println(e.getMessage()); System.out.println("error2 kmeans cluster"); } } catch (IOException ex) { Logger.getLogger(Clustering.class.getName()).log(Level.SEVERE, null, ex); System.out.println("errorrrr null kmeans"); } }
From source file:entities.ArffFile.java
/** * Dada una lista de parametros, se ejecuta el filtro de microagregacion. * Todos estos parametros son entrada del usuario. * @param df Puede ser Euclidian o Manhattan distance, se especifica en la entrada. * @param numCluster/*from www .jav a2 s . c o m*/ * @param seed * @param maxIterations * @param replaceMissingValues * @param preserveInstancesOrder * @param attributes lista de los atributos que se desean generalizar con cluster */ public void microAgregacion(DistanceFunction df, int numCluster, int seed, int maxIterations, boolean replaceMissingValues, boolean preserveInstancesOrder, List<Integer> attributes) throws Exception { //instancesFilter = new Instances(instances); SimpleKMeans kMeans; kMeans = new SimpleKMeans(); Instances uniqueAttributes; uniqueAttributes = new Instances(instancesFilter); List<String> names = new ArrayList<>(); int i = 0; for (Integer attribute : attributes) { String name = new String(instancesFilter.attribute(attribute).name()); if (instancesFilter.attribute(attribute).isDate() || instancesFilter.attribute(attribute).isString()) throw new Exception("No se puede hacer cluster con atributos de tipo DATE o STRING"); names.add(name); } while (uniqueAttributes.numAttributes() != attributes.size()) { if (!names.contains(uniqueAttributes.attribute(i).name())) uniqueAttributes.deleteAttributeAt(i); else i++; } try { kMeans.setNumClusters(numCluster); kMeans.setMaxIterations(maxIterations); kMeans.setSeed(seed); kMeans.setDisplayStdDevs(false); kMeans.setDistanceFunction(df); kMeans.setDontReplaceMissingValues(replaceMissingValues); kMeans.setPreserveInstancesOrder(preserveInstancesOrder); kMeans.buildClusterer(uniqueAttributes); //System.out.println(kMeans); for (int j = 0; j < uniqueAttributes.numInstances(); j++) { int cluster = kMeans.clusterInstance(uniqueAttributes.instance(j)); for (int k = 0; k < uniqueAttributes.numAttributes(); k++) { if (uniqueAttributes.attribute(k).isNumeric()) uniqueAttributes.instance(j).setValue(k, Double.parseDouble(kMeans.getClusterCentroids().instance(cluster).toString(k))); else uniqueAttributes.instance(j).setValue(k, kMeans.getClusterCentroids().instance(cluster).toString(k)); } } replaceValues(uniqueAttributes, attributes); } catch (Exception ex) { Logger.getLogger(ArffFile.class.getName()).log(Level.SEVERE, null, ex); } //saveToFile("4"); }
From source file:eu.cassandra.appliance.IsolatedApplianceExtractor.java
License:Apache License
/** * This is an auxiliary function that prepares the clustering data set. The * events must be translated to instances of the data set that can be used for * clustering./* ww w . ja v a2 s . c om*/ * * @param isolated * The list of the events containing an isolated appliance. * @return The instances of the data * @throws Exception */ private Instances createInstances(ArrayList<Event> isolated) throws Exception { // Initializing auxiliary variables namely the attributes of the data set Attribute id = new Attribute("id"); Attribute pDiffRise = new Attribute("pDiffRise"); Attribute qDiffRise = new Attribute("qDiffRise"); Attribute pDiffReduce = new Attribute("pDiffReduce"); Attribute qDiffReduce = new Attribute("qDiffReduce"); ArrayList<Attribute> attr = new ArrayList<Attribute>(); attr.add(id); attr.add(pDiffRise); attr.add(qDiffRise); attr.add(pDiffReduce); attr.add(qDiffReduce); Instances instances = new Instances("Isolated", attr, 0); // Each event is translated to an instance with the above attributes for (Event event : isolated) { Instance inst = new DenseInstance(5); inst.setValue(id, event.getId()); inst.setValue(pDiffRise, event.getRisingPoints().get(0).getPDiff()); inst.setValue(qDiffRise, event.getRisingPoints().get(0).getQDiff()); inst.setValue(pDiffReduce, event.getReductionPoints().get(0).getPDiff()); inst.setValue(qDiffReduce, event.getReductionPoints().get(0).getQDiff()); instances.add(inst); } int n = Constants.MAX_CLUSTERS_NUMBER; Instances newInst = null; System.out.println("Instances: " + instances.toSummaryString()); System.out.println("Max Clusters: " + n); // Create the addcluster filter of Weka and the set up the hierarchical // clusterer. AddCluster addcluster = new AddCluster(); if (instances.size() > Constants.KMEANS_LIMIT_NUMBER || instances.size() == 0) { HierarchicalClusterer clusterer = new HierarchicalClusterer(); String[] opt = { "-N", "" + n + "", "-P", "-D", "-L", "AVERAGE" }; clusterer.setDistanceFunction(new EuclideanDistance()); clusterer.setNumClusters(n); clusterer.setOptions(opt); clusterer.setPrintNewick(true); clusterer.setDebug(true); // clusterer.getOptions(); addcluster.setClusterer(clusterer); addcluster.setInputFormat(instances); addcluster.setIgnoredAttributeIndices("1"); // Cluster data set newInst = Filter.useFilter(instances, addcluster); } else { SimpleKMeans kmeans = new SimpleKMeans(); kmeans.setSeed(10); // This is the important parameter to set kmeans.setPreserveInstancesOrder(true); kmeans.setNumClusters(n); kmeans.buildClusterer(instances); addcluster.setClusterer(kmeans); addcluster.setInputFormat(instances); addcluster.setIgnoredAttributeIndices("1"); // Cluster data set newInst = Filter.useFilter(instances, addcluster); } return newInst; }
From source file:eu.cassandra.appliance.IsolatedEventsExtractor.java
License:Apache License
/** * This is an auxiliary function that prepares the clustering data set. The * events must be translated to instances of the data set that can be used for * clustering./*ww w . j a va 2 s .c o m*/ * * @param isolated * The list of the events containing an isolated appliance. * @return The instances of the data * @throws Exception */ private Instances createInstances(ArrayList<Event> isolated) throws Exception { // Initializing auxiliary variables namely the attributes of the data set Attribute id = new Attribute("id"); Attribute pDiffRise = new Attribute("pDiffRise"); Attribute qDiffRise = new Attribute("qDiffRise"); Attribute pDiffReduce = new Attribute("pDiffReduce"); Attribute qDiffReduce = new Attribute("qDiffReduce"); Attribute duration = new Attribute("duration"); ArrayList<Attribute> attr = new ArrayList<Attribute>(); attr.add(id); attr.add(pDiffRise); attr.add(qDiffRise); attr.add(pDiffReduce); attr.add(qDiffReduce); attr.add(duration); Instances instances = new Instances("Isolated", attr, 0); // Each event is translated to an instance with the above attributes for (Event event : isolated) { Instance inst = new DenseInstance(6); inst.setValue(id, event.getId()); inst.setValue(pDiffRise, event.getRisingPoints().get(0).getPDiff()); inst.setValue(qDiffRise, event.getRisingPoints().get(0).getQDiff()); inst.setValue(pDiffReduce, event.getReductionPoints().get(0).getPDiff()); inst.setValue(qDiffReduce, event.getReductionPoints().get(0).getQDiff()); inst.setValue(duration, event.getEndMinute() - event.getStartMinute()); instances.add(inst); } int n = Constants.MAX_CLUSTERS_NUMBER; Instances newInst = null; log.info("Instances: " + instances.toSummaryString()); log.info("Max Clusters: " + n); // Create the addcluster filter of Weka and the set up the hierarchical // clusterer. AddCluster addcluster = new AddCluster(); if (instances.size() > Constants.KMEANS_LIMIT_NUMBER || instances.size() == 0) { HierarchicalClusterer clusterer = new HierarchicalClusterer(); String[] opt = { "-N", "" + n + "", "-P", "-D", "-L", "AVERAGE" }; clusterer.setDistanceFunction(new EuclideanDistance()); clusterer.setNumClusters(n); clusterer.setOptions(opt); clusterer.setPrintNewick(true); clusterer.setDebug(true); // clusterer.getOptions(); addcluster.setClusterer(clusterer); addcluster.setInputFormat(instances); addcluster.setIgnoredAttributeIndices("1"); // Cluster data set newInst = Filter.useFilter(instances, addcluster); } else { SimpleKMeans kmeans = new SimpleKMeans(); kmeans.setSeed(10); // This is the important parameter to set kmeans.setPreserveInstancesOrder(true); kmeans.setNumClusters(n); kmeans.buildClusterer(instances); addcluster.setClusterer(kmeans); addcluster.setInputFormat(instances); addcluster.setIgnoredAttributeIndices("1"); // Cluster data set newInst = Filter.useFilter(instances, addcluster); } return newInst; }