Exemple #1
0
        /// <summary>
        /// Seperates a dataset into clusters or groups with similar characteristics
        /// </summary>
        /// <param name="clusterCount">The number of clusters or groups to form</param>
        /// <param name="data">An array containing data that will be clustered</param>
        /// <returns>A collection of clusters of data</returns>
        public static ClusterCollection ClusterDataSet(int clusterCount, double[,] data, string type)
        {
            //bool stableClusterFormation = false;

            int clusterNumber = 0;

            int rowCount = data.GetUpperBound(0) + 1;

            int fieldCount = data.GetUpperBound(1) + 1;

            int stableClustersCount = 0;

            int iterationCount = 0;

            double[] dataPoint;

            Random random = new Random();

            Cluster cluster = null;

            ClusterCollection clusters = new ClusterCollection();

            System.Collections.ArrayList clusterNumbers = new System.Collections.ArrayList(clusterCount);


            while (clusterNumbers.Count < clusterCount)
            {
                clusterNumber = random.Next(0, rowCount - 1);

                if (!clusterNumbers.Contains(clusterNumber))
                {
                    cluster = new Cluster();

                    clusterNumbers.Add(clusterNumber);

                    dataPoint = new double[fieldCount];


                    for (int field = 0; field < fieldCount; field++)
                    {
                        dataPoint.SetValue((data[clusterNumber, field]), field);
                    }

                    cluster.Add(dataPoint);

                    clusters.Add(cluster);
                }
            }

            int compteur = 0;

            while (stableClustersCount != clusters.Count)
            {
                stableClustersCount = 0;

                bool isNotGood = false;

                ClusterCollection newClusters = KMeans.ClusterDataSet(clusters, data, type);
                for (int tempo = 0; tempo < newClusters.Count; tempo++)
                {
                    if (newClusters[tempo].Count < 1)
                    {
                        isNotGood = true;
                    }
                }

                if (compteur == 10)
                {
                    return(clusters);
                }

                if (isNotGood)
                {
                    compteur++;
                    continue;
                }

                for (int clusterIndex = 0; clusterIndex < clusters.Count; clusterIndex++)
                {
                    double distance = 0;

                    switch (type)
                    {
                    case "DTW":
                        distance = (KMeans.DtwDistance(newClusters[clusterIndex].ClusterMean, clusters[clusterIndex].ClusterMean));
                        break;

                    case "Manhattan":
                        distance = (KMeans.ManhattanDistance(newClusters[clusterIndex].ClusterMean, clusters[clusterIndex].ClusterMean));
                        break;

                    default:
                        distance = (KMeans.EuclideanDistance(newClusters[clusterIndex].ClusterMean, clusters[clusterIndex].ClusterMean));
                        break;
                    }
                    if (distance == 0)
                    {
                        stableClustersCount++;
                    }
                }

                iterationCount++;

                clusters = newClusters;
            }

            return(clusters);
        }
Exemple #2
0
        /// <summary>
        /// Seperates a dataset into clusters or groups with similar characteristics
        /// </summary>
        /// <param name="clusterCount">The number of clusters or groups to form</param>
        /// <param name="data">An array containing data that will be clustered</param>
        /// <returns>A collection of clusters of data</returns>
        public static ClusterCollection ClusterDataSet(int clusterCount, double [,] data)
        {
            //bool stableClusterFormation = false;

            int clusterNumber = 0;

            int rowCount = data.GetUpperBound(0) + 1;

            int fieldCount = data.GetUpperBound(1) + 1;

            int stableClustersCount = 0;

            int iterationCount = 0;

            double [] dataPoint;

            Random random = new Random();

            Cluster cluster = null;

            ClusterCollection clusters = new ClusterCollection();

            System.Collections.ArrayList clusterNumbers = new System.Collections.ArrayList(clusterCount);


            while (clusterNumbers.Count < clusterCount)
            {
                clusterNumber = random.Next(0, rowCount - 1);

                if (!clusterNumbers.Contains(clusterNumber))
                {
                    cluster = new Cluster();

                    clusterNumbers.Add(clusterNumber);

                    dataPoint = new double[fieldCount];


                    for (int field = 0; field < fieldCount; field++)
                    {
                        dataPoint.SetValue((data[clusterNumber, field]), field);
                    }

                    cluster.Add(dataPoint);

                    clusters.Add(cluster);
                }
            }


            while (stableClustersCount != clusters.Count)
            {
                stableClustersCount = 0;

                ClusterCollection newClusters = KMeans.ClusterDataSet(clusters, data);

                for (int clusterIndex = 0; clusterIndex < clusters.Count; clusterIndex++)
                {
                    if ((KMeans.EuclideanDistance(newClusters[clusterIndex].ClusterMean, clusters[clusterIndex].ClusterMean)) == 0)
                    {
                        stableClustersCount++;
                    }
                }

                iterationCount++;

                clusters = newClusters;
            }

            return(clusters);
        }
Exemple #3
0
        /// <summary>
        /// Seperates a dataset into clusters or groups with similar characteristics
        /// </summary>
        /// <param name="clusterCount">The number of clusters or groups to form</param>
        /// <param name="data">An array containing data that will be clustered</param>
        /// <returns>A collection of clusters of data</returns>
        public static ClusterCollection ClusterDataSet(int clusterCount, double[,] data, string type)
        {
            //bool stableClusterFormation = false;

            int clusterNumber = 0;

            int rowCount = data.GetUpperBound(0) + 1;

            int fieldCount = data.GetUpperBound(1) + 1;

            int stableClustersCount = 0;

            int iterationCount = 0;

            double[] dataPoint;

            Random random = new Random();

            Cluster cluster = null;

            ClusterCollection clusters = new ClusterCollection();

            System.Collections.ArrayList clusterNumbers = new System.Collections.ArrayList(clusterCount);

            while (clusterNumbers.Count < clusterCount)
            {
                clusterNumber = random.Next(0, rowCount - 1);

                if (!clusterNumbers.Contains(clusterNumber))
                {

                    cluster = new Cluster();

                    clusterNumbers.Add(clusterNumber);

                    dataPoint = new double[fieldCount];

                    for (int field = 0; field < fieldCount; field++)
                    {
                        dataPoint.SetValue((data[clusterNumber, field]), field);
                    }

                    cluster.Add(dataPoint);

                    clusters.Add(cluster);
                }
            }

            int compteur = 0;
            while (stableClustersCount != clusters.Count)
            {
                stableClustersCount = 0;

                bool isNotGood = false;

                ClusterCollection newClusters = KMeans.ClusterDataSet(clusters, data, type);
                for (int tempo = 0; tempo < newClusters.Count; tempo++)
                    if (newClusters[tempo].Count < 1)
                        isNotGood = true;

                if (compteur == 10)
                    return clusters;

                if (isNotGood)
                {
                    compteur++;
                    continue;
                }

                for (int clusterIndex = 0; clusterIndex < clusters.Count; clusterIndex++)
                {
                    double distance = 0;

                    switch (type)
                    {
                        case "DTW":
                            distance = (KMeans.DtwDistance(newClusters[clusterIndex].ClusterMean, clusters[clusterIndex].ClusterMean));
                            break;
                        case "Manhattan":
                            distance = (KMeans.ManhattanDistance(newClusters[clusterIndex].ClusterMean, clusters[clusterIndex].ClusterMean));
                            break;
                        default:
                            distance = (KMeans.EuclideanDistance(newClusters[clusterIndex].ClusterMean, clusters[clusterIndex].ClusterMean));
                            break;
                    }
                    if (distance == 0)
                    {
                        stableClustersCount++;
                    }
                }

                iterationCount++;

                clusters = newClusters;
            }

            return clusters;
        }