/// <summary>
/// Creates a new <see cref="ModifiedGammaStatistic{TInstance}" /> with given dissimilarity metric.
/// </summary>
/// <param name="dissimilarityMetric">The metric used to calculate dissimilarity between cluster elements.</param>
/// <param name="centroidFunc">
/// A function to get an element representing the centroid of a <see cref="Cluster{TInstance}" />.
/// </param>
public ModifiedGammaStatistic(
IDissimilarityMetric <TInstance> dissimilarityMetric, CentroidFunction <TInstance> centroidFunc)
{
this._centroidFunc = centroidFunc;
this.DissimilarityMetric = dissimilarityMetric;
}
/// <summary>
/// Creates a new <see cref="XieBeniIndex{TInstance}" /> with given dissimilarity metric.
/// </summary>
/// <param name="dissimilarityMetric">The metric used to calculate dissimilarity between cluster elements.</param>
/// <param name="centroidFunc">
/// A function to get an element representing the centroid of a <see cref="Cluster{TInstance}" />.
/// </param>
public XieBeniIndex(
IDissimilarityMetric <TInstance> dissimilarityMetric, CentroidFunction <TInstance> centroidFunc)
{
this._centroidFunc = centroidFunc;
this.DissimilarityMetric = dissimilarityMetric;
}
/// <summary>
/// Creates a new <see cref="DaviesBouldinIndex{TInstance}" /> with given dissimilarity metric.
/// </summary>
/// <param name="dissimilarityMetric">The metric used to calculate dissimilarity between cluster elements.</param>
/// <param name="centroidFunc">
/// A function to get an element representing the centroid of a <see cref="Cluster{TInstance}" />.
/// </param>
public DaviesBouldinIndex(
IDissimilarityMetric <TInstance> dissimilarityMetric, CentroidFunction <TInstance> centroidFunc)
{
this._centroidFunc = centroidFunc;
this.DissimilarityMetric = dissimilarityMetric;
}
/// <summary>
/// Creates a new <see cref="RSquared{TInstance}" /> with given dissimilarity metric.
/// </summary>
/// <param name="dissimilarityMetric">The metric used to calculate dissimilarity between cluster elements.</param>
/// <param name="centroidFunc">
/// A function to get an element representing the centroid of a <see cref="Cluster{TInstance}" />.
/// </param>
public RSquared(IDissimilarityMetric <TInstance> dissimilarityMetric,
CentroidFunction <TInstance> centroidFunc)
{
this._centroidFunc = centroidFunc;
this.DissimilarityMetric = dissimilarityMetric;
}
/// <summary>
/// Creates a new <see cref="WithinBetweenRatio{TInstance}" /> with given dissimilarity metric.
/// </summary>
/// <param name="dissimilarityMetric">The metric used to calculate dissimilarity between cluster elements.</param>
/// <param name="centroidFunc">
/// A function to get an element representing the centroid of a <see cref="Cluster{TInstance}" />.
/// </param>
public WithinBetweenRatio(IDissimilarityMetric <TInstance> dissimilarityMetric,
CentroidFunction <TInstance> centroidFunc)
{
this._centroidFunc = centroidFunc;
this.DissimilarityMetric = dissimilarityMetric;
}
/// <summary>
/// Creates a new <see cref="RootMeanSquareStdDev{TInstance}" /> with given dissimilarity metric.
/// </summary>
/// <param name="dissimilarityMetric">The metric used to calculate dissimilarity between cluster elements.</param>
/// <param name="centroidFunc">
/// A function to get an element representing the centroid of a <see cref="Cluster{TInstance}" />.
/// </param>
public RootMeanSquareStdDev(
IDissimilarityMetric <TInstance> dissimilarityMetric, CentroidFunction <TInstance> centroidFunc)
{
this._centroidFunc = centroidFunc;
this.DissimilarityMetric = dissimilarityMetric;
}
private static void Main(string[] args)
{
var globalPerf = new PerformanceMeasure();
globalPerf.Start();
// loads points from csv file
var dataSetFile = args.Length > 0 ? args[0] : DATASET_FILE;
var filePath = Path.GetFullPath(dataSetFile);
Console.WriteLine($"Loading data-points from {filePath}...");
var parser = new CsvParser();
var dataPoints = parser.Load(filePath);
Console.WriteLine($"Clustering {dataPoints.Count} data-points...");
// performs hierarchical clustering
var clusterPerf = new PerformanceMeasure();
clusterPerf.Start();
var metric = new DataPoint(); // Euclidean distance
var linkage = new AverageLinkage <DataPoint>(metric);
var clusteringAlg = new AgglomerativeClusteringAlgorithm <DataPoint>(linkage);
var clustering = clusteringAlg.GetClustering(dataPoints);
clusterPerf.Stop();
Directory.CreateDirectory(Path.GetFullPath(RESULTS_PATH));
clustering.SaveD3DendrogramFile(Path.GetFullPath(Path.Combine(RESULTS_PATH, "dendrogram.json")));
clustering.SaveToCsv(Path.GetFullPath(Path.Combine(RESULTS_PATH, "clustering.csv")));
Console.WriteLine($"Finished clustering: {clusterPerf}");
// evaluates the clustering according to several criteria
//CentroidFunction<DataPoint> centroidFunc = DataPoint.GetMedoid;
CentroidFunction <DataPoint> centroidFunc = DataPoint.GetCentroid;
var criteria =
new Dictionary <string, IInternalEvaluationCriterion <DataPoint> >
{
{ "Silhouette coefficient", new SilhouetteCoefficient <DataPoint>(metric) },
{ "Dunn index", new DunnIndex <DataPoint>(metric) },
{ "Davies-Bouldin index", new DaviesBouldinIndex <DataPoint>(metric, centroidFunc) },
{ "Calinski-Harabasz index", new CalinskiHarabaszIndex <DataPoint>(metric, centroidFunc) },
{ "Modified Gamma statistic", new ModifiedGammaStatistic <DataPoint>(metric, centroidFunc) },
{ "Xie-Beni index", new XieBeniIndex <DataPoint>(metric, centroidFunc) },
{ "Within-Between ratio", new WithinBetweenRatio <DataPoint>(metric, centroidFunc) },
{ "I-index", new IIndex <DataPoint>(metric, centroidFunc) },
{ "Xu index", new XuIndex <DataPoint>(metric, centroidFunc) }
//{"RMSSD", new RootMeanSquareStdDev<DataPoint>(metric, centroidFunc)},
//{"R-squared", new RSquared<DataPoint>(metric, centroidFunc)},
};
foreach (var criterion in criteria)
{
GetBestPartition(clustering, criterion.Value, criterion.Key);
}
globalPerf.Stop();
Console.WriteLine($"\nFinished: {globalPerf}");
Console.ReadKey();
}
/// <summary>
/// Creates a new <see cref="DaviesBouldinIndex{TInstance}" /> with given dissimilarity metric.
/// </summary>
/// <param name="dissimilarityMetric">The metric used to calculate dissimilarity between cluster elements.</param>
/// <param name="centroidFunc">
/// A function to get an element representing the centroid of a <see cref="Cluster{TInstance}" />.
/// </param>
public CalinskiHarabaszIndex(
IDissimilarityMetric <TInstance> dissimilarityMetric, CentroidFunction <TInstance> centroidFunc)
{
this._centroidFunc = centroidFunc;
this.DissimilarityMetric = dissimilarityMetric;
}
/// <summary>
/// Creates a new <see cref="WardsMinimumVarianceLinkage{TInstance}" /> with given dissimilarity metric and centroid
/// function.
/// </summary>
/// <param name="metric">The metric used to calculate dissimilarity between cluster elements.</param>
/// <param name="centroidFunc">
/// A function to get an element representing the centroid of a <see cref="Cluster{TInstance}" />.
/// </param>
public WardsMinimumVarianceLinkage(
IDissimilarityMetric <TInstance> metric, CentroidFunction <TInstance> centroidFunc)
{
this.DissimilarityMetric = metric;
this._centroidFunc = centroidFunc;
}
