I don't have industry experience in data mining or big data so would love to hear you sharing some experience.
Do people actually run k-means, PAM, CLARA, etc. on a really big dataset? Or they just randomly pick out a sample from it? If they just take a sample of the dataset, would the result be reliable if the dataset is not normally distributed?
In practical situations when running these algorithms, can we tell how many iterations would it normally take until convergence occurs? Or the number of iterations always grow with the data size?
I'm asking this because I'm thinking of developing an approach to terminate the iterative algorithms before the convergence, and yet the results are still acceptable. I think it worths trying if the number of iterations are, say more than 1,000, so we can save some computational cost and time. What do you think?