MeanShift Clustering on dataset - matlab

Question

I have a numeric dataset and I want to cluster data with a non-parametric algorithm. Basically, I would like to cluster without specifying the number of clusters for the input. I am using this code that I accessed through the MathWorks File Exchange network which implements the Mean Shift algorithm. However, I don't Know how to adapt my data to this code as my dataset has dimensions 516 x 19.
function [clustCent,data2cluster,cluster2dataCell] =MeanShiftCluster(dataPts,bandWidth,plotFlag)
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
%perform MeanShift Clustering of data using a flat kernel
%
% ---INPUT---
% dataPts - input data, (numDim x numPts)
% bandWidth - is bandwidth parameter (scalar)
% plotFlag - display output if 2 or 3 D (logical)
% ---OUTPUT---
% clustCent - is locations of cluster centers (numDim x numClust)
% data2cluster - for every data point which cluster it belongs to (numPts)
% cluster2dataCell - for every cluster which points are in it (numClust)
%
% Bryan Feldman 02/24/06
% MeanShift first appears in
% K. Funkunaga and L.D. Hosteler, "The Estimation of the Gradient of a
% Density Function, with Applications in Pattern Recognition"
%*** Check input ****
if nargin < 2
error('no bandwidth specified')
end
if nargin < 3
plotFlag = true;
plotFlag = false;
end
%**** Initialize stuff ***
%[numPts,numDim] = size(dataPts);
[numDim,numPts] = size(dataPts);
numClust = 0;
bandSq = bandWidth^2;
initPtInds = 1:numPts
maxPos = max(dataPts,[],2); %biggest size in each dimension
minPos = min(dataPts,[],2); %smallest size in each dimension
boundBox = maxPos-minPos; %bounding box size
sizeSpace = norm(boundBox); %indicator of size of data space
stopThresh = 1e-3*bandWidth; %when mean has converged
clustCent = []; %center of clust
beenVisitedFlag = zeros(1,numPts,'uint8'); %track if a points been seen already
numInitPts = numPts %number of points to posibaly use as initilization points
clusterVotes = zeros(1,numPts,'uint16'); %used to resolve conflicts on cluster membership
while numInitPts
tempInd = ceil( (numInitPts-1e-6)*rand) %pick a random seed point
stInd = initPtInds(tempInd) %use this point as start of mean
myMean = dataPts(:,stInd); % intilize mean to this points location
myMembers = []; % points that will get added to this cluster
thisClusterVotes = zeros(1,numPts,'uint16'); %used to resolve conflicts on cluster membership
while 1 %loop untill convergence
sqDistToAll = sum((repmat(myMean,1,numPts) - dataPts).^2); %dist squared from mean to all points still active
inInds = find(sqDistToAll < bandSq); %points within bandWidth
thisClusterVotes(inInds) = thisClusterVotes(inInds)+1; %add a vote for all the in points belonging to this cluster
myOldMean = myMean; %save the old mean
myMean = mean(dataPts(:,inInds),2); %compute the new mean
myMembers = [myMembers inInds]; %add any point within bandWidth to the cluster
beenVisitedFlag(myMembers) = 1; %mark that these points have been visited
%*** plot stuff ****
if plotFlag
figure(12345),clf,hold on
if numDim == 2
plot(dataPts(1,:),dataPts(2,:),'.')
plot(dataPts(1,myMembers),dataPts(2,myMembers),'ys')
plot(myMean(1),myMean(2),'go')
plot(myOldMean(1),myOldMean(2),'rd')
pause
end
end
%**** if mean doesnt move much stop this cluster ***
if norm(myMean-myOldMean) < stopThresh
%check for merge posibilities
mergeWith = 0;
for cN = 1:numClust
distToOther = norm(myMean-clustCent(:,cN)); %distance from posible new clust max to old clust max
if distToOther < bandWidth/2 %if its within bandwidth/2 merge new and old
mergeWith = cN;
break;
end
end
if mergeWith > 0 % something to merge
clustCent(:,mergeWith) = 0.5*(myMean+clustCent(:,mergeWith)); %record the max as the mean of the two merged (I know biased twoards new ones)
%clustMembsCell{mergeWith} = unique([clustMembsCell{mergeWith} myMembers]); %record which points inside
clusterVotes(mergeWith,:) = clusterVotes(mergeWith,:) + thisClusterVotes; %add these votes to the merged cluster
else %its a new cluster
numClust = numClust+1 %increment clusters
clustCent(:,numClust) = myMean; %record the mean
%clustMembsCell{numClust} = myMembers; %store my members
clusterVotes(numClust,:) = thisClusterVotes;
end
break;
end
end
initPtInds = find(beenVisitedFlag == 0); %we can initialize with any of the points not yet visited
numInitPts = length(initPtInds); %number of active points in set
end
[val,data2cluster] = max(clusterVotes,[],1); %a point belongs to the cluster with the most votes
%*** If they want the cluster2data cell find it for them
if nargout > 2
cluster2dataCell = cell(numClust,1);
for cN = 1:numClust
myMembers = find(data2cluster == cN);
cluster2dataCell{cN} = myMembers;
end
end
This is the test code I am using to try and get the Mean Shift program to work:
clear
profile on
nPtsPerClust = 250;
nClust = 3;
totalNumPts = nPtsPerClust*nClust;
m(:,1) = [1 1];
m(:,2) = [-1 -1];
m(:,3) = [1 -1];
var = .6;
bandwidth = .75;
clustMed = [];
%clustCent;
x = var*randn(2,nPtsPerClust*nClust);
%*** build the point set
for i = 1:nClust
x(:,1+(i-1)*nPtsPerClust:(i)*nPtsPerClust) = x(:,1+(i-1)*nPtsPerClust:(i)*nPtsPerClust) + repmat(m(:,i),1,nPtsPerClust);
end
tic
[clustCent,point2cluster,clustMembsCell] = MeanShiftCluster(x,bandwidth);
toc
numClust = length(clustMembsCell)
figure(10),clf,hold on
cVec = 'bgrcmykbgrcmykbgrcmykbgrcmyk';%, cVec = [cVec cVec];
for k = 1:min(numClust,length(cVec))
myMembers = clustMembsCell{k};
myClustCen = clustCent(:,k);
plot(x(1,myMembers),x(2,myMembers),[cVec(k) '.'])
plot(myClustCen(1),myClustCen(2),'o','MarkerEdgeColor','k','MarkerFaceColor',cVec(k), 'MarkerSize',10)
end
title(['no shifting, numClust:' int2str(numClust)])
The test script generates random data X. In my case. I want to use the matrix D of size 516 x 19 but I am not sure how to adapt my data to this function. The function is returning results that are not agreeing with my understanding of the algorithm.
Does anyone know how to do this?