I use Lloyd's algorithm for clustering. Since it relies on a random initialization and Lloyd's algorithm can get stuck in local optima of the k-means objective function, I have to run it several times.
How many different random initializations should I perform with Lloyd's algorithm to obtain the optimal clustering with X% of confidence? Or at least is there any rule of thumb advising for a decent number of random initializations?