public static BayesianClusterMultinetClassifier CreateAntClustBMNClassifier_MB(int seed, Dataset dataset, int maxIterations, int colonySize, int convergence, int clustersNumber, ISimilarityMeasure similarityMeasure, IClassificationQualityMeasure classificationMeasure, IClassificationAlgorithm algorithm, bool fireEvents)
{
DataMining.Utilities.RandomUtility.Initialize(seed);
DefaultHeuristicCalculator <int> calculator = new DefaultHeuristicCalculator <int>();
ClusteringMBInvalidator invalidator = new ClusteringMBInvalidator();
ClusteringClassificationQualityEvaluator evaluator = new ClusteringClassificationQualityEvaluator(classificationMeasure, algorithm);
evaluator.Dataset = dataset;
KMeansLocalSearch localSearch = new KMeansLocalSearch(dataset, 1, similarityMeasure, evaluator);
Problem <int> problem = new Problem <int>(invalidator, calculator, evaluator, localSearch);
AntClustBMN_MB antClustBMN = new AntClustBMN_MB(maxIterations, colonySize, convergence, problem, clustersNumber, similarityMeasure, dataset, true, algorithm, classificationMeasure);
antClustBMN.OnPostColonyIteration += new EventHandler(antClustering_OnPostColonyIteration);
return(antClustBMN.CreateClassifier() as BayesianClusterMultinetClassifier);
}
public static ClusteringSolution CreateACOClusters_MB(int seed, Dataset dataset, int clustersNumber, ISimilarityMeasure similarityMeasure, int maxIterations, int colonySize, int convergenceIterations, bool fireEvents, bool performLocalSearch)
{
DataMining.Utilities.RandomUtility.Initialize(seed);
DefaultHeuristicCalculator <int> calculator = new DefaultHeuristicCalculator <int>();
ClusteringMBInvalidator invalidator = new ClusteringMBInvalidator();
DataMining.ProximityMeasures.IClusteringQualityMeasure measure = new CohesionClusteringMeasure();
ClusteringQualityEvaluator cohesionEvaluator = new ClusteringQualityEvaluator(measure);
KMeansLocalSearch localSearch = new KMeansLocalSearch(dataset, 1, similarityMeasure, cohesionEvaluator);
ACO.ProblemSpecifics.ISolutionQualityEvaluator <int> evaluator = new ClusteringQualityEvaluator(measure);
Problem <int> problem = new Problem <int>(invalidator, calculator, evaluator, localSearch);
ACOClustering_MB antClustering = new ACOClustering_MB(maxIterations, colonySize, convergenceIterations, problem, clustersNumber, similarityMeasure, dataset, performLocalSearch);
antClustering.OnPostColonyIteration += new EventHandler(antClustering_OnPostColonyIteration);
return(antClustering.CreateClusters());
}
public static void TestACOCluster_MBThenBMN()
{
int seed = (int)DateTime.Now.Ticks;
Console.WriteLine("Start");
string datasetFile = folderPath + "\\" + datasetName + ".arff";
Dataset trainingSet = ArffHelper.LoadDatasetFromArff(datasetFile);
Dataset testingSet = ArffHelper.LoadDatasetFromArff(datasetFile);
double avgQualiy = 0;
for (int i = 0; i < 1; i++)
{
DataMining.ProximityMeasures.ISimilarityMeasure similarityMeasure = new DataMining.ProximityMeasures.ClassBasedSimilarityMeasure(trainingSet);
DataMining.ClassificationMeasures.IClassificationQualityMeasure accuracy = new DataMining.ClassificationMeasures.AccuracyMeasure();
DataMining.Algorithms.IClassificationAlgorithm naive = new NaiveBayesAlgorithm();
DefaultHeuristicCalculator <int> calculator = new DefaultHeuristicCalculator <int>();
ClusteringMBInvalidator invalidator = new ClusteringMBInvalidator();
DataMining.ProximityMeasures.IClusteringQualityMeasure measure = new CohesionClusteringMeasure();
ClusteringQualityEvaluator cohesionEvaluator = new ClusteringQualityEvaluator(measure);
KMeansLocalSearch localSearch = new KMeansLocalSearch(trainingSet, 1, similarityMeasure, cohesionEvaluator);
ACO.ProblemSpecifics.ISolutionQualityEvaluator <int> evaluator = new ClusteringQualityEvaluator(measure);
Problem <int> problem = new Problem <int>(invalidator, calculator, evaluator, localSearch);
DataMining.Algorithms.IClusteringAlgorithm AntClustering = new ACOClustering_MB(1000, 10, 10, problem, 6, similarityMeasure, true);
DataMining.Model.IClassifier cBMNClassifier = SingleTest.CreateClusteringBMNClassifier(seed, 6, trainingSet, similarityMeasure, accuracy, AntClustering, naive, true);
double quality = SingleTest.TestClassifier(cBMNClassifier, testingSet, accuracy);
Console.WriteLine("Quality: " + quality.ToString());
avgQualiy += quality;
}
Console.WriteLine(avgQualiy / 10);
Console.WriteLine("End");
}
