public static void TestKMeans()
{
string datasetFile = folderPath + "\\" + datasetName + ".arff";
Dataset dataset = ArffHelper.LoadDatasetFromArff(datasetFile);
DataMining.ProximityMeasures.ISimilarityMeasure similarityMeasure = new DataMining.ProximityMeasures.BinaryMatchingSimilarityMeasure();
DataMining.ProximityMeasures.IClusteringQualityMeasure cohesionEvaluator = new DataMining.ProximityMeasures.CohesionClusteringMeasure();
double maxQuality = 0;
double avgQuality = 0;
for (int i = 0; i < 1; i++)
{
int seed = (int)DateTime.Now.Ticks;
DataMining.Model.ClusteringSolution solution = SingleTest.CreateKMeansClusters(seed, dataset, 5, similarityMeasure, 1000, true);
double currentQuality = cohesionEvaluator.CalculateQuality(solution);
Console.WriteLine("Iteration " + i.ToString() + ":" + currentQuality.ToString());
avgQuality += currentQuality;
if (currentQuality > maxQuality)
{
maxQuality = currentQuality;
}
}
// avgQuality /= 10;
// Console.WriteLine("Maximum:" + maxQuality.ToString());
// Console.WriteLine("Average:" + avgQuality.ToString());
}
public static void TestACOCluster_MB()
{
int seed = (int)DateTime.Now.Ticks;
Console.WriteLine("Start");
string datasetFile = folderPath + "\\" + datasetName + ".arff";
Dataset trainingSet = ArffHelper.LoadDatasetFromArff(datasetFile);
Dataset testingSet = ArffHelper.LoadDatasetFromArff(datasetFile);
DataMining.ProximityMeasures.ISimilarityMeasure similarityMeasure = new DataMining.ProximityMeasures.ClassBasedSimilarityMeasure(trainingSet);
DataMining.ProximityMeasures.IClusteringQualityMeasure cohesionEvaluator = new DataMining.ProximityMeasures.CohesionClusteringMeasure();
DataMining.Model.ClusteringSolution solution = SingleTest.CreateACOClusters_MB(seed, trainingSet, 3, similarityMeasure, 1000, 10, 10, true, true);
double quality = cohesionEvaluator.CalculateQuality(solution);
Console.WriteLine("Final Quality:" + quality.ToString());
Console.WriteLine("End");
}
