kcenter_dd.m

数据挖掘的工具箱,最新版的,希望对做这方面研究的人有用

%KCENTER_DD k-center data description.
% 
%       W = kcenter_dd(A,fracrej,K)
% 
% Train a k-center method with K prototypes on dataset A.
% 
% See also datasets, mappings, dd_roc

% Copyright: D.M.J. Tax, R.P.W. Duin, [e-mail]
% Faculty of Applied Physics, Delft University of Technology
% P.O. Box 5046, 2600 GA Delft, The Netherlands
  
function [W,out] = kcenter_dd(a,fracrej,K,nrtries)

if nargin < 4, nrtries = 25; end
if nargin < 3 | isempty(K), K = 5; end
if nargin < 2 | isempty(fracrej), fracrej = 0.05; end
if nargin < 1 | isempty(a) % empty nndd
	W = mapping(mfilename,{fracrej,K,nrtries});
	W = setname(W,'K-Centers data description');
	return
end


if ~ismapping(fracrej)           %training

	a = +target_class(a);     % make sure a is an OC dataset
	k = size(a,2);

	% train it:
	D = sqrt(sqeucldistm(a,a));
	[lab,J,dmin] = kcentres(D,K,nrtries);
	x = a(J,:);

	% obtain the threshold:
	d = sqrt(min(sqeucldistm(a,x),[],2));
	thr = dd_threshold(d,1-fracrej);

	%and save all useful data:
	W.x = x;
	W.threshold = thr;
	W.scale = mean(d);
	W = mapping(mfilename,'trained',W,str2mat('target','outlier'),k,2);
	W = setname(W,'K-Centers data description');

else                               %testing

	W = getdata(fracrej);  % unpack
	m = size(a,1);

	%compute:
	out = [sqrt(min(sqeucldistm(+a,W.x),[],2)) repmat(W.threshold,m,1)];
	newout = dist2dens(out,W.scale);

	W = setdat(a,newout,fracrej);
end
return