public static void TestKmeanClusteringBMN()
{
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;
int k = 8;
for (int i = 0; i < 10; i++)
{
DataMining.ProximityMeasures.ISimilarityMeasure similarityMeasure = new DataMining.ProximityMeasures.ClassBasedSimilarityMeasure(trainingSet);
//DataMining.ProximityMeasures.ISimilarityMeasure similarityMeasure = new DataMining.ProximityMeasures.BinaryMatchingSimilarityMeasure();
DataMining.ClassificationMeasures.IClassificationQualityMeasure accuracy = new DataMining.ClassificationMeasures.AccuracyMeasure();
DataMining.Algorithms.IClassificationAlgorithm naive = new NaiveBayesAlgorithm();
DataMining.Algorithms.IClusteringAlgorithm kmeans = new DataMining.Algorithms.KMeans(trainingSet, k, similarityMeasure, 100, true);
DataMining.Model.IClassifier cBMNClassifier = SingleTest.CreateClusteringBMNClassifier(seed, k, trainingSet, similarityMeasure, accuracy, kmeans, naive, false);
double quality = SingleTest.TestClassifier(cBMNClassifier, testingSet, accuracy);
Console.WriteLine("Quality: " + quality.ToString());
avgQualiy += quality;
}
Console.WriteLine(avgQualiy / 10);
Console.WriteLine("End");
}
public static void TestACOCluster_IBThenBMN()
{
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.ProximityMeasures.ISimilarityMeasure similarityMeasure = new DataMining.ProximityMeasures.BinaryMatchingSimilarityMeasure();
DataMining.ClassificationMeasures.IClassificationQualityMeasure accuracy = new DataMining.ClassificationMeasures.AccuracyMeasure();
DataMining.Algorithms.IClassificationAlgorithm naive = new NaiveBayesAlgorithm();
DefaultHeuristicCalculator <ClusterExampleAssignment> calculator = new DefaultHeuristicCalculator <ClusterExampleAssignment>();
ClusteringIBInvalidator invalidator = new ClusteringIBInvalidator();
DataMining.ProximityMeasures.IClusteringQualityMeasure measure = new CohesionClusteringMeasure();
ClusteringQualityEvaluator cohesionEvaluator = new ClusteringQualityEvaluator(measure);
KMeansLocalSearch localSearch = new KMeansLocalSearch(trainingSet, 1, similarityMeasure, cohesionEvaluator);
ACO.ProblemSpecifics.ISolutionQualityEvaluator <DataMining.Model.ClusterExampleAssignment> evaluator = new ClusteringQualityEvaluator(measure);
Problem <DataMining.Model.ClusterExampleAssignment> problem = new Problem <DataMining.Model.ClusterExampleAssignment>(invalidator, calculator, evaluator, localSearch);
DataMining.Algorithms.IClusteringAlgorithm AntClustering = new ACOClustering_IB(1000, 10, 10, problem, 10, similarityMeasure, true);
DataMining.Model.IClassifier cBMNClassifier = SingleTest.CreateClusteringBMNClassifier(seed, 10, 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");
}