k-means empty cluster
I try to implement k-means as a homework. My exercise sheet gives me following remark regarding empty centers:

    "During the iterations, if any of the cluster centers has no data points associated with it, replace it with a random data point."

That confuses me a bit, firstly Wikipedia or other sources I read do not mention that at all. I further read about a problem with 'choosing a good k for your data' - how is my algorithm supposed to converge if I start setting new centers for cluster that were empty.

If I ignore empty clusters I converge after 30-40 iterations. Is it wrong to ignore empty clusters? 
k-means empty cluster
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I try to implement k-means as a homework. My exercise sheet gives me following remark regarding empty centers:

    "During the iterations, if any of the cluster centers has no data points associated with it, replace it with a random data point."

That confuses me a bit, firstly Wikipedia or other sources I read do not mention that at all. I further read about a problem with 'choosing a good k for your data' - how is my algorithm supposed to converge if I start setting new centers for cluster that were empty.

If I ignore empty clusters I converge after 30-40 iterations. Is it wrong to ignore empty clusters? 