Implementation example of Dbscan algorithm in JSP

Dbscan is a density based clustering algorithm, its basic principle is given two parameters, ξ and MINP, where ξ can be understood as a radius, the algorithm will look for a sample in this radius, MINP is a ξ for the radius of the number of samples found in the limit of N, as long as the N>=MINP, Find the sample point is the core sample point, the specific description of the algorithm see reference file 1, below is the Java implementation of this algorithm:
First, define a point class that represents the sample points

<!--[endif]-->

　　

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+ ">";

}

}

Then define a tool class that serves the implementation of the algorithm:

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 two points

* @param P Point

* @param q Dot

* @return return 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 to point is not the core

* @param lst the list of storage points

* @param p points to be tested

* @param e e radius

* @param minp density threshold

* @return temporary storage of visited points

*/

public static list<point> Iskeypoint (list<point> lst,point p,int e,int) {

int count=0;

List<point> tmplst=new arraylist<point> ();

For (iterator<point> 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<point> lst) {

For (iterator<point> it=lst.iterator (); It.hasnext ();) {

Point P=it.next ();

if (!p.isclassed ()) {

P.setclassed (TRUE);

}

}

}

/**

* If B contains the elements contained in a, then the two sets

Merge

* @param a

* @param b

* @return A

*/

public static Boolean mergelist (list<point> a,list<point> b) {

Boolean merge=false;

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

if (A.contains (B.get (index)) {

Merge=true;

Break

}

}

if (merge) {

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

if (!a.contains (B.get (index)) {

A.add (B.get (index));

}

}

}

return merge;

}

/**

* Returns the collection of points in the text

* @return Returns the collection of the midpoint of the text

* @throws IOException

*/

public static list<point> Getpointslist () throws ioexception{

List<point> lst=new arraylist<point> ();

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, the algorithm is implemented in the main program, as follows:

Package com.sunzhenxing;

Import java.io.*;

Import java.util.*;

public class Dbscan {

private static list<point> pointslist=new arraylist<point> ();//Storing a collection of all points

private static list<list<point>> resultlist=new arraylist<list<point>> ();// The result set returned by the storage Dbscan algorithm

private static int e=2;//e radius

private static int minp=3;//density threshold value

/**

* Extract all points in the text and store them in Pointslist

* @throws IOException

*/

private static void display () {

int index=1;

For (iterator<list<point>> it=resultlist.iterator (); It.hasnext ();) {

List<point> Lst=it.next ();

if (Lst.isempty ()) {

Continue

}
SYSTEM.OUT.PRINTLN ("-----" +index+ "cluster-----");
For (iterator<point> it1=lst.iterator (); It1.hasnext ();) {

Point P=it1.next ();

System.out.println (P.print ());

}

index++;

}

}

Find all the clustering that can go direct

private static void Applydbscan () {

try {

Pointslist=utility.getpointslist ();

For (iterator<point> it=pointslist.iterator (); It.hasnext ();) {

Point P=it.next ();

if (!p.isclassed ()) {

List<point> tmplst=new arraylist<point> ();

if ((Tmplst=utility.iskeypoint (Pointslist, P, E, MINP))!= null) {

Mark all points that have been assembled

Utility.setlistclassed (TMPLST);

Resultlist.add (TMPLST);

}

}

}

catch (IOException e) {

TODO auto-generated Catch block

E.printstacktrace ();

}

}

Merge all the clustering that can be reached directly, that is, find the indirect points and merge them.

private static list<list<point>> GetResult () {

Applydbscan ()//Find all direct clustering

int length=resultlist.size ();

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

for (int j=i+1;j<length;++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 (0,0), new Point (0,2));

}

}

　　

Below is a small test, that is, using the contents of the Src\com\sunzhenxing\points.txt file for testing, Points.txt file content is: