I'm running into a problem while working on clustering. I work on data with white Gaussian noise. All of the methods I have come across use some sort of random initialization to set up the mean and covariance matrix of the clusters.
My question is: Since the initialization is random, there is a chance that I get a really bad starting point which gives me bad results. How do I deal with this?
One specific initialization I'm considering is the K-Means++ which is better than strictly random because it at least attempts to use the data to make informed initialization, but it too is random in the end.
Do people usually do multiple runs and take the best initialization?
What about that for streaming data?