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##### LkPPC

A Matlab code for local k-Proximal Plane Clustering. (You could Right-Click [Code] , and Save, then you can download the whole matlab code.)

##### Reference

Yuan-Hai Shao,Yan-Ru Guo and Zhi-Min Yang. Local k-Proximal Plane Clustering,2013,Submitted.

##### Main Function

Need stdata,adjacency,GepOneSide,getcu,getcX,GetchushiW function.

function pY =LkPPC(X,cX,k,hknn,c,g,W); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % LkPPC: local k-Proximal Plane Clustering % % pY =LkPPC(X,cX,k,hknn,c,g,W); % % Input: X - data points matrix. Each row vector of fea is a data point % cX- the data points who is used to construct the initial plane % k- number of cluster; % hknn- the upper bound of the KNN; % W- the construct the initial plane; % c: [0,inf] appropriate parameter to tune the weight. % g:[0,1] is used to control the localization of the clustering plane. % Output: pY - Predict the class of X. % % % Examples: % X = rand(50,10);Y=[ones(20,1); ones(20,1)+1; ones(10,1)+2]; % c=0.01; g=0.1; k=3; % pY =LkPPC(X,cX,k,hknn,c,g,W); % % Reference: % Yuan-Hai Shao,Yan-Ru Guo and Zhi-Min Yang. Local k-Proximal Plane Clustering,2013,Submitted. % % Version 1.0 --Dec/2013 % % Written by Yan-Ru Guo ("Guoyanru211@163.com") %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Initailization %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %tic; [s,t]=size(X);hknn=20;B=1;knn=1; bknn=[];cX=[];ccY=[];i=1; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Each data point is normalized with the mean 0 and standard deviation 1. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% stX=stdata(X); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Use KNN to find cX: the initial points %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% [cX,bknn]= getcX(stX,k,hknn); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Construct the initial plane W %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% W=GetchushiW(cX,k,c); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Update plane W: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% pYnew=zeros(s,1);[s,t]=size(stX);[mm,nn]=size(cX);pY=crossvalind('kfold',s,k); z=0;q=zeros(k,t); V=zeros(s,k); while(~isempty(find(pY~=pYnew, 1)) && z~=1000) pYnew=pY; z=z+1; % update W for i=1:k tA=stX((pY==i),:); tB=stX((pY~=i),:); mi=size(tA,1); if ~isempty(find(pY==i, 1)) W(i,:)=GepOneSide(tA,tB,c); q(i,:)=sum(tA)/mi; end end for l=1:s for ff=1:k V(l,ff)=(norm((stX(l,:)-q(ff,:)),2)^2); end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Predict and output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% pY=abs(stX*W(:,1:t)'+ones(s,1)*W(:,t+1)')+g*V(:,:); [tmp,pY]=min(pY'); pY=pY'; end
##### Contacts

Any question or advice please email to shaoyuanhai21@163.com and Guoyanru211@163.com.

• Last updated: December 27, 2013