This is a pseudocode for DBSCAN from "".

And I have tried the pseudocode from the  page. I prefer this.

ExpandCluster(SetOfPoints, Point, ClId, Eps, MinPts) : Boolean;	seeds:=SetOfPoints.regionQuery(Point,Eps);	IF seeds.size
     
       Empty DO			currentP := seeds.first();			result := SetOfPoints.regionQuery(currentP, Eps);			IF result.size >= MinPts THEN				FOR i FROM 1 TO result.size DO					resultP := result.get(i);					IF resultP.ClId IN {UNCLASSIFIED, NOISE} THEN						IF resultP.ClId = UNCLASSIFIED THEN							seeds.append(resultP);						END IF;						SetOfPoints.changeClId(resultP,ClId);					END IF; // UNCLASSIFIED or NOISE				END FOR;			END IF; // result.size >= MinPts			seeds.delete(currentP);		END WHILE; // seeds <> Empty		RETURN True;	END IFEND; // ExpandClusterDBSCAN (SetOfPoints, Eps, MinPts)// SetOfPoints is UNCLASSIFIED	ClusterId := nextId(NOISE);	FOR i FROM 1 TO SetOfPoints.size DO		Point := SetOfPoints.get(i);		IF Point.ClId = UNCLASSIFIED THEN			IF ExpandCluster(SetOfPoints, Point, ClusterId, Eps, MinPts) THEN				ClusterId := nextId(ClusterId)			END IF		END IF	END FOREND; // DBSCAN
     

This can cluster the data based on density(or distance) consider noise, it's no complicated to implement.

I see that the most important thing that to get good result is to get good eps and minpts. and if your data is complicate the distance of two data point have so much influence on the result.

 

Good luck.