K-Means and Expectation Maximization (EM) implemented on n-Dimensional UCI dataset

Please find my full source code from the link.... K-Means and EM project with UCI sample

Some part of Expectation Maximization code is as follows:

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 /// <summary>

    /// Run K-Mean untill clusters not change and centroid converged

    /// </summary>

    public class EM

    {

        /// <summary>

        /// A set of n dimentional points

        /// </summary>

        private DataSet _dataSet;

        /// <summary>

        /// Entry point of EM algorithm

        /// </summary>

        /// <param name="dataSet"></param>

        /// <param name="K"></param>

        /// <param name="distanceMethod"></param>

        /// <param name="centroidMethod"></param>

        public static void Run(DataSet dataSet, int K, int distanceMethod = 1, int centroidMethod = 1)

        {

            var clusters = new Cluster[K];

            if (K > dataSet.DataPoints.Count)

            {

                throw new Exception("Clusters are more thn data points choosen");

            }

            ChooseRandomCentroids(dataSet.DataPoints, clusters);

            int _iteration = 1;

            while (true)

            {

                Console.WriteLine("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@");

                Console.WriteLine("@@@@@@@@@@@@ Iteration: {0} @@@@@@@@@@@@@@@@", _iteration++);

                Console.WriteLine("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@");

                clusters.ToList().ForEach(c => c.ClusterPoints.Clear());

                // Step 1: Find Euclidean Distances

                List<double[]> table = new List<double[]>();

                foreach (var dataPoint in dataSet.DataPoints)

                {

                    List<double> row =new List<double>();

                    foreach (var cluster in clusters)

                    {

                        var theDistance = DistanceMeasure.SquaredEuclideanDistance(dataPoint, cluster.Centroid);

                        row.Add(theDistance);

                    }

                    table.Add(row.ToArray());

                }

                // Step 2: E-Step

                foreach (var row in table)

                {

                    var sum = row.Sum();

                    //calculation step

                    var row2 = (double[]) row.Clone();

                    for (int j = 0; j < row.Length; j++)

                    {

                        row2[row.Length-1-j] = row[j] / sum;

                    }

                    //copy back

                    for (int j = 0; j < row.Length; j++)

                    {

                        row[j] = row2[j];

                    }

                   

                }

                // Step 3: M-Step

                foreach (var row in table)

                {

                    for (int j = 0; j < row.Length; j++)

                    {

                        row[j] *= row[j];

                    }

                }

                for (int i = 0; i < table[0].Length; i++)

                {

                    for (int j = 0; j < dataSet.DataPoints.Count; j++)

                    {

                        var dataPoint = DataPoint.Clone(dataSet.DataPoints[j]);

                        for (int k = 0; k < dataPoint.Vector.Length; k++)

                        {

                            dataPoint.Vector[k] *= table[j][i];

                        }

                        clusters[i].ClusterPoints.Add(dataPoint);

                    }

                }

                for (int i = 0; i < clusters.Length; i++)

                {

                    var cluster = clusters[i];

                    var dimensions = cluster.ClusterPoints[0].Dimensions;

                    var newCentroid = new DataPoint { Vector = new double[dimensions] };

                    double sum1 = 0, sum2 = 0;

                    for (int j = 0; j < table.Count; j++)

                    {

                        sum1 += table[j][i];

                    }

                    for (int k = 0; k < dimensions; k++)

                    {

                        sum2 = cluster.ClusterPoints.Sum(x => x.Vector[k]);

                        newCentroid.Vector[k] = sum2 / sum1;

                    }

                    cluster.OldCentroid = cluster.Centroid;

                    cluster.Centroid = newCentroid;

                      Console.Write(cluster.ToString());

                }

                bool allClustersConverged = true;

                foreach (var cluster in clusters)

                {

                    allClustersConverged = allClustersConverged & cluster.IsConverged();

                }

                if (allClustersConverged)

                    break;

            }

        }

        /// <summary>

        /// Used to choose K (# of clusters) initial points

        /// </summary>

        /// <param name="points"></param>

        /// <param name="clusters"></param>

        private static void ChooseRandomCentroids(IList<DataPoint> points, Cluster[] clusters)

        {

            if (points.Count < 2)

            {

                throw new Exception("Points are insufficient to start the algorithm");

            }

            if (clusters != null && clusters.Length < 2)

            {

                throw new Exception("Clusters are insufficient to start the algorithm");

            }

            Random rand = new Random();

            HashSet<int> results = new HashSet<int>();

            while (results.Count < clusters.Length)

            {

                results.Add(rand.Next(1, points.Count));

            }

            //randomly choose first points and choose second which extream distance from first

            for (int j = 0; j < results.Count; j++)

            {

                clusters[j] = new Cluster {Centroid = points[j]};

            }

        }