DBSCAN算法的java实现

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander and Xiaowei Xu in 1996. It is a density based clustering algorithm because it finds a number of clusters starting from the estimated density distribution of corresponding nodes. DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature.

DBSCAN是一种基于密度的聚类算法，它的基本原理就是给定两个参数，ξ和minp，其中 ξ可以理解为半径，算法将在这个半径内查找样本，minp是一个以ξ为半径查找到的样本个数n的限制条件，只要n>=minp，查找到的样本点就是核心样本点，算法的具体描述见参考文件1，下边是这个算法的java实现：

首先定义一个Point类，代表样本点：

  1: package com.sunzhenxing;

  2: 

  3: public class Point {

  4:   private int x;

  5:   private int y;

  6:   private boolean isKey;

  7:   private boolean isClassed;

  8:   

  9:   public boolean isKey() {

 10:     return isKey;

 11:   }

 12:   public void setKey(boolean isKey) {

 13:     this.isKey = isKey;

 14:     this.isClassed=true;

 15:   }

 16:   public boolean isClassed() {

 17:     return isClassed;

 18:   }

 19:   public void setClassed(boolean isClassed) {

 20:     this.isClassed = isClassed;

 21:   }

 22:   public int getX() {

 23:     return x;

 24:   }

 25:   public void setX(int x) {

 26:     this.x = x;

 27:   }

 28:   public int getY() {

 29:     return y;

 30:   }

 31:   public void setY(int y) {

 32:     this.y = y;

 33:   }

 34:   

 35:   public Point(){

 36:     x=0;

 37:     y=0;

 38:   }

 39:   public Point(int x,int y){

 40:     this.x=x;

 41:     this.y=y;

 42:   }

 43:   public Point(String str){

 44:     String[] p=str.split(",");

 45:     this.x=Integer.parseInt(p[0]);

 46:     this.y=Integer.parseInt(p[1]);

 47:   }

 48:   public String print(){

 49:     return "<"+this.x+","+this.y+">";

 50:   }

 51: }

 52: 

然后定义一个工具类，为算法的实现服务：

  1: package com.sunzhenxing;

  2: 

  3: import java.io.BufferedReader;

  4: import java.io.FileReader;

  5: import java.io.IOException;

  6: import java.util.*;

  7: 

  8: public final class Utility {

  9:   //计算两点之间的距离

 10:   public static double getDistance(Point p,Point q){

 11:     int dx=p.getX()-q.getX();

 12:     int dy=p.getY()-q.getY();

 13:     double distance=Math.sqrt(dx*dx+dy*dy);

 14:     return distance;

 15:   }

 16:   //检测p点是不是核心点，tmpLst存储核心点的直达点

 17:   public static List<Point> isKeyPoint(List<Point> lst,Point p,int e,int minp){

 18:     int count=0;

 19:     List<Point> tmpLst=new ArrayList<Point>();

 20:     for(Iterator<Point> it=lst.iterator();it.hasNext();){

 21:       Point q=it.next();

 22:       if(getDistance(p,q)<=e){

 23:         ++count;

 24:         if(!tmpLst.contains(q)){

 25:           tmpLst.add(q);

 26:         }

 27:       }

 28:     }

 29:     if(count>=minp){

 30:       p.setKey(true);

 31:       return tmpLst;

 32:     }

 33:     return null;

 34:   }

 35:   //合并两个链表，前提是b中的核心点包含在a中

 36:   public static boolean mergeList(List<Point> a,List<Point> b){

 37:     boolean merge=false;

 38:     if(a==null || b==null){

 39:       return false;

 40:     }

 41:     for(int index=0;index<b.size();++index){

 42:       Point p=b.get(index);

 43:       if(p.isKey() && a.contains(p)){

 44:         merge=true;

 45:         break;

 46:       }

 47:     }

 48:     if(merge){

 49:       for(int index=0;index<b.size();++index){

 50:         if(!a.contains(b.get(index))){

 51:           a.add(b.get(index));

 52:         }

 53:       }

 54:     }

 55:     return merge;

 56:   }

 57:   //获取文本中的样本点集合

 58:   public static List<Point> getPointsList() throws IOException{

 59:     List<Point> lst=new ArrayList<Point>();

 60:     String txtPath="src\\com\\sunzhenxing\\points.txt";

 61:     BufferedReader br=new BufferedReader(new FileReader(txtPath));

 62:     String str="";

 63:     while((str=br.readLine())!=null && str!=""){

 64:       lst.add(new Point(str));

 65:     }

 66:     br.close();

 67:     return lst;

 68:   }

 69:   //显示聚类的结果

 70:   public static void display(List<List<Point>> resultList){

 71:     int index=1;

 72:     for(Iterator<List<Point>> it=resultList.iterator();it.hasNext();){

 73:       List<Point> lst=it.next();

 74:       if(lst.isEmpty()){

 75:         continue;

 76:       }

 77:       System.out.println("-----第"+index+"个聚类-----");

 78:       for(Iterator<Point> it1=lst.iterator();it1.hasNext();){

 79:         Point p=it1.next();

 80:         System.out.println(p.print());

 81:       }

 82:       index++;

 83:     }

 84:   }

 85: }

 86: 

最后在主程序中实现算法，如下所示：

  1: package com.sunzhenxing;

  2: 

  3: import java.io.IOException;

  4: import java.util.*;

  5: 

  6: public class Dbscan {

  7:   private final static int e=2;//ε半径

  8:   private final static int minp=4;//密度阈值

  9:   private static List<Point> pointsList=new ArrayList<Point>();//存储原始样本点

 10:   private static List<List<Point>> resultList=new ArrayList<List<Point>>();//存储最后的聚类结果

 11:   

 12:   private static void applyDbscan() throws IOException{

 13:     pointsList=Utility.getPointsList();

 14:     for(int index=0;index<pointsList.size();++index){

 15:       List<Point> tmpLst=new ArrayList<Point>();

 16:       Point p=pointsList.get(index);

 17:       if(p.isClassed())

 18:         continue;

 19:       tmpLst=Utility.isKeyPoint(pointsList, p, e, minp);

 20:       if(tmpLst!=null){

 21:         resultList.add(tmpLst);

 22:       }

 23:     }

 24:     int length=resultList.size();

 25:     for(int i=0;i<length;++i){

 26:       for(int j=0;j<length;++j){

 27:         if(i!=j){

 28:           if(Utility.mergeList(resultList.get(i), resultList.get(j))){

 29:             resultList.get(j).clear();

 30:           }

 31:         }

 32:       }

 33:     }

 34:   }

 35:   public static void main(String[] args) {

 36:     try {

 37:       //调用DBSCAN的实现算法

 38:       applyDbscan();

 39:       Utility.display(resultList);

 40:     } catch (IOException e) {

 41:       // TODO Auto-generated catch block

 42:       e.printStackTrace();

 43:     }

 44:     

 45:   }

 46: 

 47: }

 48: 

下边是一个小测试， 即使用src\\com\\sunzhenxing\\points.txt文件的内容进行测试，points.txt的文件内容是：

0,0
0,1
0,2
0,3
0,4
0,5
12,1
12,2
12,3
12,4
12,5
12,6
0,6
0,7
12,7
0,8
0,9
1,1

最后算法的结果是：

-----第1个聚类-----
<0,0>
<0,1>
<0,2>
<1,1>
<0,3>
<0,4>
<0,5>
<0,6>
<0,7>
<0,8>
<0,9>
-----第2个聚类-----
<12,1>
<12,2>
<12,3>
<12,4>
<12,5>
<12,6>
<12,7>
大家画一下坐标就可以理解实验的结论了。