kmeans_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

%KMEANS_DD k-means data description.
% 
%       W = KMEANS_DD(A,FRACREJ,K)
% 
% Train a k-means method with K prototypes on dataset A. Parameter
% fracrej gives the fraction of the target set which will be rejected.
% 
% Optionally, one may give the error tolerance as last argument as
% stopping criterion.
% 
% See also datasets, mappings, dd_roc

% Copyright: D. 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] = kmeans_dd(a,fracrej,K,errtol)

if nargin < 4,  errtol = 1e-5; end
if nargin < 3 | isempty(K), K = 5; end
if nargin < 2 | isempty(fracrej), fracrej = 0.05; end
if nargin < 1 | isempty(a) % empty dd
	W = mapping('kmeans_dd',{fracrej,K,errtol});
	W = setname(W,'K-Means 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:
  [labs,w] = mykmeans(a,K,errtol);

  % obtain the threshold:
  d = sqrt(min(sqeucldistm(a,w),[],2));
  if (size(d,2)~=1)
    d = d';
  end
  thr = dd_threshold(d,1-fracrej);

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

else                               %testing

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

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

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

return