Different Values if K-means Clustring on same data.

I have been using matlab function of K-means clustring for making clusters of data. I happen to apply it on same data. But got wildly different results every time. I know the reason for this. But I need sugestions for overcoming this issue. Should I use some modified version of K-means or Should look for some other clustering technique?

K-means command which i used is "kmeans(Feature_Matrix,20,'Replicates',5,'emptyaction','singleton');

Answer 1

[Shashank Prasanna]

Kmeans can get stuck in local minima. By which I mean it is sensitive to initial centroid positions. You can specify a higher number of replicates to increase you chances of getting a global solution.

If you are interested in exploring other clustering algorithms, find all the supported ones here:

http://www.mathworks.com/machine-learning/

[Khawaja Asim]

thanks...

[Walter Roberson]

kmeans uses random initialization of cluster positions, unless you pass it specific positions to start at.