This is expected behavior because KMEANS by default selects the initial cluster centroid positions at random (albeit from the observations). That is, the value of the 'start' parameter is set to 'sample' as can be seen from the documentation. Another outcome you would also observe if you run your code several times is that KMEANS errors out because an empty cluster is created at the first iteration (i.e., idx is all 1's or all 2's). You could always pass a matrix of initial positions as the value for the 'start' parameter, for example:
[idx c] = kmeans(a,2,'start',[0 0.5]')
This would yield the same result every time but since the partition returned by KMEANS highly depends on the initial centroid positions, you would probably get a sub-optimal partition (unless your provide a "lucky" vector for the 'start' parameter). The typical use of KMEANS entails setting the 'Replicates' parameter to an integer n corresponding to the number of times to repeat the clustering. KMEANS then returns the partition with the lowest sum, over all clusters, of the within-cluster sums of point-to-cluster-centroid distances.
