用C#实现k均值聚类算法 C# C# PAUL-MAPLE（Using C# to achieve K means clustering algorithm C#, C#, PAUL-MAPLE）.doc

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用C#实现k均值聚类算法 C# C# PAUL-MAPLE（Using C# to achieve K means clustering algorithm C#, C#, PAUL-MAPLE）.doc

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用C#实现k均值聚类算法 C# C# PAUL-MAPLE（Using C# to achieve K means clustering algorithm C#, C#, PAUL-MAPLE）

用C#实现k均值聚类算法 C# C# PAUL-MAPLE（Using C# to achieve K means clustering algorithm C#, C#, PAUL-MAPLE） K mean algorithm is a pattern recognition problem of poly classification, which is implemented using C# algorithm Here is the program source code: Using System; Using System.Drawing; Using System.Collections; Using System.ComponentModel; Using System.Windows.Forms; Using System.Data; Namespace KMean_win { X The Form1 / / / note. X Public class Form1: System.Windows.Forms.Form { X The designer must always variable. X Private, System.ComponentModel.Container, components = null; Private static int k = 2; / / class number, this example into 2 categories Private static int total = 20; / / point number Private int test = 0; Private PointF[] unknown = new PointF[total]; / / set points Private int[] type = new int[total]; / / each temporary class Public PointF[] z = new PointF[k]; / / save the new clustering center Public PointF[] Z0 = new PointF[k]; / / keep on a cluster center Private PointF sum; Private int temp = 0; Private System.Windows.Forms.TextBox textBox1; Private Int l = 0; / / the number of iterations / / constructor initialization Public, Form1 () { Unknown[0]=new Point (0,0); Unknown[1]=new Point (1,0); Unknown[2]=new Point (0,1); Unknown[3]=new Point (1,1); Unknown[4]=new Point (2,1); Unknown[5]=new Point (1,2); Unknown[6]=new Point (2,2); Unknown[7]=new Point (3,2); Unknown[8]=new Point (6,6); Unknown[9]=new Point (7,6); Unknown[10]=new Point (8,6); Unknown[11]=new Point (6,7); Unknown[12]=new Point (7,7); Unknown[13]=new Point (8,7); Unknown[14]=new Point (9,7); Unknown[15]=new Point (7,8); Unknown[16]=new Point (8,8); Unknown[17]=new Point (9,8); Unknown[18]=new Point (8,9); Unknown[19]=new Point (9,9); InitializeComponent (); Test = 0; Choice K initial clustering center z[i] For (int, i=0, I, z[i] = unknown[i]); For (int, i=0, I, type[i] = 0); } A new clustering center / / calculation Public PointF newCenter (int m) { Int N = 0; For (int i=0; I { if ([i] = m