how to cluster colors using weka jar in java

We will begin by describing basic concepts and ideas. Moreover, I will briefly explain how an open-source Java implementation of K-Means, offered in the SPMF data mining library can be used. I will explain what is the goal of clustering, and then introduce the popular K-Means algorithm with an example. If you get errors that classes are not found, check your CLASSPATH: does it include weka.jar You can explicitly set CLASSPATHvia the -cpcommand line option as well. In this blog post, I will introduce the popular data mining task of clustering (also called cluster analysis). For instance, assuming that these files are in the current directory, the command to issue is: java -cp weka.jar weka-spectral-clusterer.jar colt.jar That’s all folk. heap size for your java engine, usually via -Xmx1024Mor -Xmx1024mfor 1GB - the default setting of 16 to 64MB is usually too small. bug('Clustering eval took ' + (timeTakenEval / 1000) + ' s. To run WEKA, the Java runtime’s classpath should simply include the following jars: weka.jar, colt.jar, and weka-spectral-clusterer.jar. bug('Clustering took ' + (timeTaken / 1000) + ' s.') bug('ClusterEvaluator results: ' + clusterEval.clusterResultsToString()) ĭouble acc = EvaluationUtils.predictAccuracy(insts, clusterEval.getClassesToClusters(), clusterEval.getClusterAssignments()) Long timeTakenEval = System.currentTimeMillis() - timeStartEval Long timeTaken = System.currentTimeMillis() - timeStart Long timeStartEval = System.currentTimeMillis() ĬlusterEvaluation clusterEval = new ClusterEvaluation() tDistanceFunction(new EuclideanDistance()) Ĭlusterer.buildClusterer(removedClassInstances) SimpleKMeans kMeans = (SimpleKMeans) clusterer.getClusterer() Public static double performKernelClustering(final Instances instances, final int numThreads) throws Exception )

How to cluster colors using weka jar in java