如何wix 做 网站,网站建设合同 售后维护期,wordpress hermit,推广方式英语1 聚类算法
聚类分析或简单聚类基本上是一种无监督的学习方法#xff0c;它将数据点划分为若干特定的批次或组#xff0c;使得相同组中的数据点具有相似的属性#xff0c;而不同组中的数据点在某种意义上具有不同的属性。它包括许多基于差分进化的不同方法。
E、 g.K-均值… 1 聚类算法
聚类分析或简单聚类基本上是一种无监督的学习方法它将数据点划分为若干特定的批次或组使得相同组中的数据点具有相似的属性而不同组中的数据点在某种意义上具有不同的属性。它包括许多基于差分进化的不同方法。
E、 g.K-均值点之间的距离、亲和传播图距离、均值偏移点之间的距离、DBSCAN最近点之间的距离、高斯混合到中心的马氏距离、谱聚类图距离等。
基本上所有聚类方法都使用相同的方法即首先计算相似度然后使用相似度将数据点聚类为组或批。在这里我们将重点讨论基于密度的应用程序空间聚类和噪声DBSCAN聚类方法。
簇是数据空间中的密集区域由点密度较低的区域隔开。DBSCAN算法基于“簇”和“噪声”这一直观概念。关键思想是对于簇的每个点给定半径的邻域必须至少包含最少数量的点。
2 DBSCAN
DBSCAN全称为Density-based Spatial Clustering of Applications with Noise。翻译中文即基于密度的噪声应用空间聚类DBSCAN
基于密度的噪声应用空间聚类是一种基于密度的聚类技术。在基于密度的聚类中数据按数据点高度集中的区域分组数据点高度集中的区域被数据点高度集中的区域包围。基本上该算法会找到数据点密集的地方并调用这些集群。 DBSCAN是基于密度的聚类的基本算法。它可以从包含噪声和异常值的大量数据中发现不同形状和大小的聚类。DBSCAN集群最吸引人的特性是它对异常值的鲁棒性。该算法只需要两个参数即minPoints和epsilon。对于一个区域聚集在一起的最小点数阈值是minpoint其中作为epsilon的是用于在任何点的邻域中定位点的距离度量。DBSCAN围绕每个数据点创建一个epsilon半径的圆并将其分类为核心点、边界点和噪声。 3 源程序 using System; using System.Data; using System.Collections.Generic; using System.Drawing;
namespace Legalsoft.Truffer.Algorithm { public class K_Means_DBSCAN_Algorithm { public ListPoint Cluster1 new ListPoint(); public ListPoint Cluster2 new ListPoint(); public ListPoint afterCluster1 new ListPoint(); public ListPoint afterCluster2 new ListPoint(); public void Caverage() { afterCluster1.Clear(); afterCluster2.Clear(); ListPoint Cluster new ListPoint(); 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; while (flag) { 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); } } } /// summary /// K-Means-DBSCAN算法 /// /summary public void DBSCAN(int radius 1, int MinPts 12) { double pow 2.0; afterCluster1.Clear(); afterCluster2.Clear(); ListPoint Cluster new ListPoint(); ListPoint temp1 new ListPoint(); ListPoint temp2 new ListPoint(); 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; 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; 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; } } } } } 4 源代码
using System;
using System.Data;
using System.Collections.Generic;
using System.Drawing;namespace Legalsoft.Truffer.Algorithm
{public class K_Means_DBSCAN_Algorithm{public ListPoint Cluster1 new ListPoint();public ListPoint Cluster2 new ListPoint();public ListPoint afterCluster1 new ListPoint();public ListPoint afterCluster2 new ListPoint();public void Caverage(){afterCluster1.Clear();afterCluster2.Clear();ListPoint Cluster new ListPoint();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;while (flag){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);}}}/// summary/// K-Means-DBSCAN算法/// /summarypublic void DBSCAN(int radius 1, int MinPts 12){double pow 2.0;afterCluster1.Clear();afterCluster2.Clear();ListPoint Cluster new ListPoint();ListPoint temp1 new ListPoint();ListPoint temp2 new ListPoint();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;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;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;}}}}
}
