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Hilft es dir denn wenn ich dir ein Matlab-code von DBSCAN sende um die Funktion in VBA umzuwandeln?
Matlabcode con DBSCAN:
% -------------------------------------------------------------------------
% Function: [class,type]=dbscan(x,k,Eps)
% -------------------------------------------------------------------------
% Aim:
% Clustering the data with Density-Based Scan Algorithm with Noise (DBSCAN)
% -------------------------------------------------------------------------
% Input:
% x - data set (m,n); m-objects, n-variables
% k - number of objects in a neighborhood of an object
% (minimal number of objects considered as a cluster)
% Eps - neighborhood radius, if not known avoid this parameter or put []
% -------------------------------------------------------------------------
% Output:
% class - vector specifying assignment of the i-th object to certain
% cluster (m,1)
% type - vector specifying type of the i-th object
% (core: 1, border: 0, outlier: -1)
% -------------------------------------------------------------------------
% Example of use:
% x=[randn(30,2)*.4;randn(40,2)*.5+ones(40,1)*[4 4]];
% [class,type]=dbscan(x,5,[])
% clusteringfigs('Dbscan',x,[1 2],class,type)
% -------------------------------------------------------------------------
% References:
% [1] M. Ester, H. Kriegel, J. Sander, X. Xu, A density-based algorithm for
% discovering clusters in large spatial databases with noise, proc.
% 2nd Int. Conf. on Knowledge Discovery and Data Mining, Portland, OR, 1996,
% p. 226, available from:
% www.dbs.informatik.uni-muenchen.de/cgi-bin/papers?query=--CO
% [2] M. Daszykowski, B. Walczak, D. L. Massart, Looking for
% Natural Patterns in Data. Part 1: Density Based Approach,
% Chemom. Intell. Lab. Syst. 56 (2001) 83-92
% -------------------------------------------------------------------------
% Written by Michal Daszykowski
% Department of Chemometrics, Institute of Chemistry,
% The University of Silesia
% December 2004
% http://www.chemometria.us.edu.pl
function [class,type]=dbscan(x,k,Eps)
[m,n]=size(x);
if nargin<3 | isempty(Eps)
[Eps]=epsilon(x,k);
end
x=[[1:m]' x];
[m,n]=size(x);
type=zeros(1,m);
no=1;
touched=zeros(m,1);
for i=1:m
if touched(i)==0;
ob=x(i,:);
D=dist(ob(2:n),x(:,2:n));
ind=find(D<=Eps);
if length(ind)>1 & length(ind)<k+1
type(i)=0;
class(i)=0;
end
if length(ind)==1
type(i)=-1;
class(i)=-1;
touched(i)=1;
end
if length(ind)>=k+1;
type(i)=1;
class(ind)=ones(length(ind),1)*max(no);
while ~isempty(ind)
ob=x(ind(1),:);
touched(ind(1))=1;
ind(1)=[];
D=dist(ob(2:n),x(:,2:n));
i1=find(D<=Eps);
if length(i1)>1
class(i1)=no;
if length(i1)>=k+1;
type(ob(1))=1;
else
type(ob(1))=0;
end
for i=1:length(i1)
if touched(i1(i))==0
touched(i1(i))=1;
ind=[ind i1(i)];
class(i1(i))=no;
end
end
end
end
no=no+1;
end
end
end
i1=find(class==0);
class(i1)=-1;
type(i1)=-1;
%...........................................
function [Eps]=epsilon(x,k)
% Function: [Eps]=epsilon(x,k)
%
% Aim:
% Analytical way of estimating neighborhood radius for DBSCAN
%
% Input:
% x - data matrix (m,n); m-objects, n-variables
% k - number of objects in a neighborhood of an object
% (minimal number of objects considered as a cluster)
[m,n]=size(x);
Eps=((prod(max(x)-min(x))*k*gamma(.5*n+1))/(m*sqrt(pi.^n))).^(1/n);
%............................................
function [D]=dist(i,x)
% function: [D]=dist(i,x)
%
% Aim:
% Calculates the Euclidean distances between the i-th object and all objects in x
%
% Input:
% i - an object (1,n)
% x - data matrix (m,n); m-objects, n-variables
%
% Output:
% D - Euclidean distance (m,1)
[m,n]=size(x);
D=sqrt(sum((((ones(m,1)*i)-x).^2)'));
if n==1
D=abs((ones(m,1)*i-x))';
end
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