DBSCAN is a density-based clustering algorithm, and its basic principle is to a given two parameters, ξ and minp, where ξ can be interpreted as the radius, the algorithm will search within a radius of the sample, minp is a ξ To find the radius of the sample number n of constraints, as long as n> = minp, find the sample point is the core of the sample points, the proposed algorithm are described in References 1, below is a java implementation of this algorithm: …
DBSCAN is a density-based clustering algorithm, and its basic principle is given two parameters, ξ and minp, where ξ can be interpreted as the radius, the algorithm will find the sample in this radius, minp is a search radius ξ n number of samples to restrictions, as long as n> = minp, find the sample point is the core of the sample points, the proposed algorithm are described in References 1, below is a java implementation of this algorithm:
First define a Point class, the representative sample points
package com.sunzhenxing;
public class Point (
private int x;
private int y;
private boolean isKey;
private boolean isClassed;
public boolean isKey () (
return isKey;
)
public void setKey (boolean isKey) (
this.isKey = isKey;
this.isClassed = true;
)
public boolean isClassed () (
return isClassed;
)
public void setClassed (boolean isClassed) (
this.isClassed = isClassed;
)
public int getX () (
return x;
)
public void setX (int x) (
this.x = x;
)
public int getY () (
return y;
)
public void setY (int y) (
this.y = y;
)
public Point () (
x = 0;
y = 0;
)
public Point (int x, int y) (
this.x = x;
this.y = y;
)
public Point (String str) (
String [] p = str.split (“,”);
this.x = Integer.parseInt (p [0]);
this.y = Integer.parseInt (p [1]);
)
public String print () (
return “<” + this.x +”,”+ this.y +”>”;
)
)
And then define a utility class, for the algorithm implementation services:
package com.sunzhenxing;
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util .*;
public class Utility (
/ **
* Test the distance between the two points
* @ Param p point
* @ Param q point
* @ Return returns the distance between two points
* /
public static double getDistance (Point p, Point q) (
int dx = p.getX ()-q.getX ();
int dy = p.getY ()-q.getY ();
double distance = Math.sqrt (dx * dx + dy * dy);
return distance;
) / **
* Check the given point is not the central point
* @ Param lst The list storage point
* @ Param p the point to be tested
* @ Param ee radius
* @ Param minp density threshold
* @ Return a temporary storage point visited
* /
public static List
isKeyPoint (List
lst, Point p, int e, int minp) (int count = 0;
List
tmpLst = new ArrayList
();for (Iterator
it = lst.iterator (); it.hasNext ();){Point q = it.next ();
if (getDistance (p, q) <= e) (
+ + Count;
if (! tmpLst.contains (q)) (
tmpLst.add (q);
)
)
)
if (count> = minp) (
p.setKey (true);
return tmpLst;
)
return null;
)
public static void setListClassed (List
lst) (for (Iterator
it = lst.iterator (); it.hasNext ();){Point p = it.next ();
if (! p.isClassed ()) (
p.setClassed (true);
)
)
)
/ **
Merge
* @ Param a
* @ Param b
* @ Return a
* /
public static boolean mergeList (List
a, List
b) (boolean merge = false;
for (int index = 0; index
if (a.contains (b.get (index))) (
merge = true;
break;
)
)
if (merge) (
for (int index = 0; index
if (! a.contains (b.get (index))) (
a.add (b.get (index));
)
)
)
return merge;
)
/ **
* Back to the collection point in the text
* @ Return back to the mid-point of the set text
* @ Throws IOException
* /
public static List
getPointsList () throws IOException (List
lst = new ArrayList
();String txtPath = “src \ \ com \ \ sunzhenxing \ \ points.txt”;
BufferedReader br = new BufferedReader (new FileReader (txtPath));
String str = “”;
while ((str = br.readLine ())!= null & & str !=”"){
lst.add (new Point (str));
)
br.close ();
return lst;
)
)
Finally, in the main program to implement algorithm, as follows:
package com.sunzhenxing;
import java.io. *;
import java.util .*;
public class Dbscan (
private static List
pointsList = new ArrayList
();// store the set of all pointsprivate static List
> resultList = new ArrayList
>();// storage DBSCAN algorithm to return the result set
private static int e = 2; / / e radius
private static int minp = 3; / / density threshold
/ **
* Extract all the points in the text and stored in the pointsList
* @ Throws IOException
* /
private static void display () (
int index = 1;
for (Iterator
> it = resultList.iterator (); it.hasNext ();){
if (lst.isEmpty ()) (
continue;
) System.out.println (“—– s “+ index +” a cluster —–”);
for (Iterator
it1 = lst.iterator (); it1.hasNext ();){Point p = it1.next ();
System.out.println (p.print ());
)
index + +;
)
)
/ / Find all the cluster can be directly
private static void applyDbscan () (
try (
pointsList = Utility.getPointsList ();
for (Iterator
it = pointsList.iterator (); it.hasNext ();){Point p = it.next ();
if (! p.isClassed ()) (
List
tmpLst = new ArrayList
();if ((tmpLst = Utility.isKeyPoint (pointsList, p, e, minp))! = null) (
/ / End point for all clustering to mark
Utility.setListClassed (tmpLst);
resultList.add (tmpLst);
)
)
)
) Catch (IOException e) (
/ / TODO Auto-generated catch block
e.printStackTrace ();
)
)
/ / Direct access to the clustering of all the merger, that is up to the point and find the indirect merger
private static List
> getResult () (
applyDbscan ();// find all the direct clustering
int length = resultList.size ();
for (int i = 0; i
for (int j = i +1; j
if (Utility.mergeList (resultList.get (i), resultList.get (j))) (
resultList.get (j). clear ();
)
)
)
return resultList;
)
/ **
* Program main function
* @ Param args
* /
public static void main (String [] args) (
getResult ();
display ();
/ / System.out.println (Utility.getDistance (new Point (0,0), new Point (0,2)));
)
)
Below is a small test, that is used src \ \ com \ \ sunzhenxing \ \ points.txt contents of the file test, points.txt the file contents are:
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
The final result of the algorithm is:
—– —– 1st cluster
<0,0>
<0,1>
<0,2>
<1,1>
<0,3>
<0,4>
<0,5>
<0,6>
<0,7>
<0,8>
<0,9>
—– —– 2nd cluster
<12,1>
<12.2>
<12.3>
<12,4>
<12,5>
<12.6>
<12,7>
Coordinates we can draw what conclusions the experiment to understand.
