MATLAB: Use kmeans to split database of flowers

k-meansStatistics and Machine Learning Toolbox

Hi, My project is about flower recognition using matlab. I have database of 100 flower of 11 types. The purpose of the project is to get as input one of the flowers and print the name and features of the flower.

I already extract 4 features of the flowers( rgb colors, number of petals and etc.) Now I want to use 'kmeans' in order to devide the flowers into 11 clusters, when each type will be assign to one of the clusters. Does kmeans has option that will let me choose 11 of the flowers as seeds which will build the clusters? Or mayby you know about anoher function which will let me insert the flowers into 11 clusters manualy?

Thanks (^_^)

Best Answer

You can specify your 'seed' as start point for KMEANS:

[idx,ctrs] = kmeans(X,11,'start',seedmat)

X is your matrix of 100 rows and 4 columns (based on your description)

seedmat is a matrix of your 11x4 seeds centroids which is where KMEANS will start to search.

http://www.mathworks.com/help/stats/kmeans.html

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MATLAB: K-means Clustering Result Always Changes

%generate some initial cluster centers according to some deterministic algorithm
%in this case, I construct a space-diagonal equally spaced, but choose your
%own algorithm
minc = min(cobat, 1);
maxc = max(cobat, 1);
nsamp = size(cobat,1);
initialcenters = repmat(minc, nsamp, 1) + bsxfun(@times, (0:nsamp-1).', (maxc - minc) ./ (nsamp-1));
%Once you have constructed the initial centers, cluster using those centers
[cidx ctrs] = kmeans(cobat, k, 'dist', 'sqEuclidean', 'start', initialcenters);

MATLAB: I need to know each point to which cluster in kmeans fun.

Huda, you gave kmeans two points and asked it to cluster them into two clusters. It has assigned the first point to cluster 1, whose centroid is at the first point, and similarly for the second point. Presumably that is not very informative.

I don't know what your data mean, or whether kmeans makes sense, but your description sounds like something more suited to distance-based methods such as hierarchical clustering or multidimensional scaling, both of which are available in the Statistics Toolbox. You would, of course, have to convert your similarities to dissimilarities.

Best Answer

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MATLAB: K-means Clustering Result Always Changes

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