c# c均值聚类及DBSCAN聚类

winform的界面搭建比较简单，首先在界面上的左边产生两堆特征值为 0-100的二维样本。通过 c均值聚类及DBSCAN聚类将聚类结果显示在右边。样本点有x和y值，用List<Point>存储一个类。

using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Windows.Forms;
using System.Drawing.Text;namespace PatternRecon
{public partial class Form1 : Form{public List<Point> Cluster1=new List<Point>();public List<Point> Cluster2=new List<Point>();public List<Point> afterCluster1 = new List<Point>();public List<Point> afterCluster2 = new List<Point>();public Form1(){InitializeComponent();InitCombo();}public void InitCombo(){this.comboBox1.Items.Add("c均值");this.comboBox1.Items.Add("DBSCAN");//设置默认值this.textBox1.Text = "1";this.textBox2.Text = "12";//设置背景afterCluster1.Clear();afterCluster2.Clear();this.pictureBox1.Image = (DrawCluster(afterCluster1, afterCluster2, false));this.pictureBox2.Image = (DrawCluster(afterCluster1, afterCluster2, false));}//产生随机数种子public static int GetRandomSeed(){byte[] bytes = new byte[4];System.Security.Cryptography.RNGCryptoServiceProvider rng = new System.Security.Cryptography.RNGCryptoServiceProvider();rng.GetBytes(bytes);return BitConverter.ToInt32(bytes, 0);}//产生标准正太分布public static double[] NormalDistribution(){Random rand = new Random(GetRandomSeed());double[] y;double u1, u2, v1=0, v2=0, s = 0, z1=0, z2=0;while (s > 1 || s == 0){u1 = rand.NextDouble();u2 = rand.NextDouble();v1 = 2 * u1 - 1;v2 = 2 * u2 - 1;s = v1 * v1 + v2 * v2;}z1 = Math.Sqrt(-2 * Math.Log(s) / s) * v1;z2 = Math.Sqrt(-2 * Math.Log(s) / s) * v2;y = new double[] { z1, z2 };return y; //返回两个服从正态分布N(0,1)的随机数z0 和 z1}//确定private void button1_Click(object sender, EventArgs e){if (this.comboBox1.Text == "c均值"){//调用c均值Caverage();this.pictureBox2.Image = (DrawCluster(afterCluster1, afterCluster2,false));}else if (this.comboBox1.Text == "DBSCAN"){//调用DBSCANif (this.textBox1.Text == "" || this.textBox2.Text == ""){MessageBox.Show("请输入DBSCAN值");}else{DBSCAN();this.pictureBox2.Image = (DrawCluster(afterCluster1, afterCluster2, false));}}else{MessageBox.Show("请选择聚类方法！");}}//绘图public Bitmap DrawCluster(List<Point> m1,List<Point> m2,bool flag){Bitmap bmp = new Bitmap(405, 405);Graphics g = Graphics.FromImage(bmp);Pen p = new Pen(Color.Black, 2);Pen linep = new Pen(Color.FromArgb(30, Color.Black), 2);Pen pm1;Pen pm2;if (flag==true){pm1 = new Pen(Color.Red, 2);pm2 = new Pen(Color.Green, 2);}else {pm1 = new Pen(Color.Brown, 2);pm2 = new Pen(Color.Blue, 2);}Point p1 = new Point(400, 0);Point p2 = new Point(400, 400);Point p3 = new Point(0, 400);Point p4 = new Point(400, 400);try{//画线g.Clear(Color.White);g.DrawLine(p, p1, p2);g.DrawLine(p, p3, p4);g.DrawLine(p,new Point(0,0), p1);g.DrawLine(p, new Point(0, 0), p3);g.DrawString("高斯分布随机产生100个样品，特征值取值为0-10", new Font("宋体", 10), new SolidBrush(Color.Black), new PointF(10, 10));for (int i = 1; i <= 9; i++){g.DrawLine(linep, new Point(i * 40, 400), new Point(i * 40, 0));g.DrawLine(linep, new Point(400, i * 40), new Point(0, i * 40));g.DrawString(i.ToString(), new Font("宋体", 10), new SolidBrush(Color.Blue), new PointF(i * 40, 400 - 15));g.DrawString(i.ToString(), new Font("宋体", 10), new SolidBrush(Color.Blue), new PointF(400 - 15, i * 40));}//画点foreach(Point m1Point in m1){g.DrawEllipse(pm1, m1Point.X, m1Point.Y, 2, 2);}foreach (Point m1Point in m2){g.DrawEllipse(pm2, m1Point.X, m1Point.Y, 2, 2);}}catch (Exception ex){MessageBox.Show(ex.Message);}finally{g.Dispose();p.Dispose();pm1.Dispose();pm2.Dispose();linep.Dispose();}return bmp;//描点}//用于产生两个均值public int count = 0;private void pictureBox1_MouseClick(object sender, MouseEventArgs e){count++;// MessageBox.Show(count.ToString());if (count==1){for (int i = 0; i < 50;i++ ){double[] point = NormalDistribution();double x = point[0] +e.X/40;double y = point[1] +e.Y/40;int x1 = (int)(x * 40);int x2 = (int)(y * 40);Cluster1.Add(new Point(x1,x2));}}if (count == 2){for (int i = 0; i < 50; i++){double[] point = NormalDistribution();double x = point[0] + e.X / 40;double y = point[1] + e.Y / 40;int x1 = (int)(x * 40);int x2 = (int)(y * 40);Cluster2.Add(new Point(x1, x2));}this.pictureBox1.Image=(DrawCluster(Cluster1,Cluster2,true));}}//c均值，按照PPT上的步骤写的public void Caverage(){this.afterCluster1.Clear();this.afterCluster2.Clear();//初始化聚类中心List<Point> Cluster=new List<Point>();foreach(Point m1Point in Cluster1){Cluster.Add(m1Point);}foreach(Point m2Point in Cluster2){Cluster.Add(m2Point);}Point C1=Cluster[0];Point C2=Cluster[1];bool flag= true;//判断聚类中心是否相等while (flag){// MessageBox.Show("聚类中心1:" + C1.X.ToString() + " " + C1.Y.ToString() + "聚类中心2:" + C2.X.ToString() + " " + C2.Y.ToString());int N1 = 0;int N2 = 0;int C1x = 0;int C2x = 0;int C1y = 0;int C2y = 0;foreach (Point point in Cluster){int s1 = Math.Abs(point.X - C1.X) + Math.Abs(point.Y - C1.Y);int s2 = Math.Abs(point.X - C2.X) + Math.Abs(point.Y - C2.Y);if(s1<s2){N1++;C1x += point.X;C1y += point.Y;}else{N2++;C2x += point.X;C2y += point.Y;  }}if(C1x/N1==C1.X&&C2.X==C2x/N2&& C1.Y==C1y/N1&& C2.Y==C2y/N2){flag = false;}C1.X=C1x/N1;C2.X=C2x/N2;C1.Y=C1y/N1;C2.Y=C2y/N2;}foreach (Point point in Cluster){int s1 = Math.Abs(point.X - C1.X) + Math.Abs(point.Y - C1.Y);int s2 = Math.Abs(point.X - C2.X) + Math.Abs(point.Y - C2.Y);if (s1 < s2){afterCluster1.Add(point);}else{afterCluster2.Add(point);}}}//DBSCANpublic void DBSCAN(){try{double pow = 2.0;int radius = int.Parse(this.textBox1.Text) * 40;int MinPts = int.Parse(this.textBox2.Text);this.afterCluster1.Clear();this.afterCluster2.Clear();List<Point> Cluster = new List<Point>();List<Point> temp1 = new List<Point>();List<Point> temp2 = new List<Point>();foreach (Point m1Point in Cluster1){Cluster.Add(m1Point);}foreach (Point m2Point in Cluster2){Cluster.Add(m2Point);}Point C1;Point C2;bool isC1Get = false;bool isC2Get = false;foreach (Point mm in Cluster){if (isC1Get == false){int count = 0;//记数temp1.Clear();foreach (Point mm1 in Cluster){double banjing = Math.Sqrt(Math.Pow(mm1.X - mm.X, pow) + Math.Pow(mm1.Y - mm.Y, pow));if (banjing < radius){count++;temp1.Add(mm1);}}if (count >= MinPts){C1 = mm;isC1Get = true;foreach (Point mm2 in temp1){foreach (Point mm3 in Cluster){double banjing = Math.Sqrt(Math.Pow(mm3.X - mm2.X, pow) + Math.Pow(mm3.Y - mm2.Y, pow));if (banjing < radius && (afterCluster1.Contains(mm3) == false)){afterCluster1.Add(mm3);}}}}}else if (isC2Get == false){if (afterCluster1.Contains(mm) == false){int count = 0;//记数temp2.Clear();foreach (Point mm1 in Cluster){double banjing = Math.Sqrt(Math.Pow(mm1.X - mm.X, pow) + Math.Pow(mm1.Y - mm.Y, pow));if (banjing < radius){count++;temp2.Add(mm1);}}if (count >= MinPts){C2 = mm;isC2Get = true;foreach (Point mm2 in temp2){foreach (Point mm3 in Cluster){double banjing = Math.Sqrt(Math.Pow(mm3.X - mm2.X, pow) + Math.Pow(mm3.Y - mm2.Y, pow));if (banjing < radius && (afterCluster1.Contains(mm3) == false) && afterCluster2.Contains(mm3) == false){afterCluster2.Add(mm3);}}}}}else{continue;}}else{break;}}}catch(Exception ex){MessageBox.Show(ex.Message);}}//清除private void button2_Click(object sender, EventArgs e){this.Cluster1.Clear();this.Cluster2.Clear();this.afterCluster1.Clear();this.afterCluster2.Clear();count = 0;Bitmap bmp = new Bitmap(405, 405);Graphics g = Graphics.FromImage(bmp);g.Clear(Color.White);g.Dispose();this.pictureBox1.Image = (DrawCluster(afterCluster1, afterCluster2, false));this.pictureBox2.Image = (DrawCluster(afterCluster1, afterCluster2, false));}//圈出不同,圈的之后注意比较一下中心private void button3_Click(object sender, EventArgs e){Bitmap bmp = (Bitmap)this.pictureBox2.Image;Graphics g = Graphics.FromImage(bmp);Pen p = new Pen(Color.Black,2);int count1 = 0;int count2 = 0;List<Point> belongCluster1;List<Point> belongCluster2;foreach (Point m1Point in Cluster1){if (afterCluster1.Contains(m1Point))count1++;if (afterCluster2.Contains(m1Point))count2++;}if (count1 > count2){belongCluster1 = afterCluster1;belongCluster2 = afterCluster2;}else{belongCluster1 = afterCluster2;belongCluster2 = afterCluster1;}//画出不同的地方foreach (Point m1Point in Cluster1){if (belongCluster1.Contains(m1Point) == false){g.DrawEllipse(p, m1Point.X, m1Point.Y, 6, 6);}}foreach (Point m1Point in Cluster2){if (belongCluster2.Contains(m1Point) == false){g.DrawRectangle(p, m1Point.X, m1Point.Y, 6, 6);}}g.Dispose();p.Dispose();this.pictureBox2.Image = bmp;}}
}

最终的聚类结果：

demo下载：https://download.csdn.net/download/cc_fys/10770325

github demo下载：https://github.com/ccessl/cluster

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