Hi,
I perform Kmeans algorithm with k=3, at every run I get a new order for the centroid values:
a. mu = 79.41 151.34 185.45
b. mu = 185.45 79.41 151.34
c. mu = 185.45 151.34 79.41
so how I can stabilize the centroid order to get the correspond class label?
thank you
Hi,
I didn't clearly understand your question but you can simply sort the values of the centroids (Increasing or Decreasing) and the clusters will be also sorted as well.
Hi,
Thank you, Yacine
this is functions correctly in the case of features vector contains the pixel intensity.
k-means clustering algorithm
k-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed apriori. The main idea is to define k centers, one for each cluster. These centers should be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each other. The next step is to take each point belonging to a given data set and associate it to the nearest center. When no point is pending, the first step is completed and an early group age is done. At this point we need to re-calculate k new centroids as barycenter of the clusters resulting from the previous step. After we have these k new centroids, a new binding has to be done between the same data set points and the nearest new center. A loop has been generated. As a result of this loop we may notice that the k centers change their location step by step until no more changes are done or in other words centers do not move any more. Finally, this algorithm aims at minimizing an objective function know as squared error function given by:
where,
‘||xi - vj||’ is the Euclidean distance between xi and vj.
‘ci’ is the number of data points in ith cluster.
‘c’ is the number of cluster centers.
Algorithmic steps for k-means clustering
Let X = {x1,x2,x3,……..,xn} be the set of data points and V = {v1,v2,…….,vc} be the set of centers.
1) Randomly select ‘c’ cluster centers.
2) Calculate the distance between each data point and cluster centers.
3) Assign the data point to the cluster center whose distance from the cluster center is minimum of all the cluster centers..
4) Recalculate the new cluster center using:
where, ‘ci’ represents the number of data points in ith cluster.
5) Recalculate the distance between each data point and new obtained cluster centers.
6) If no data point was reassigned then stop, otherwise repeat from step 3).
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