We does he say K-means isn't great? It always seemed like it would be useful if you have lots of data.
You're kind of looking for the most similar records to match each record to. Am I understanding that right?
You're kind of looking for the most similar records to match each record to. Am I understanding that right?
In the K-means case, it is good to know beforehand how many groups(clusters) you are expecting. Otherwise, there are other clustering algorithms(like gdbscan) which detect the number of cluster automatically(although you have to provide the average expected density). But once again all depends on the question you want to answer, or if you are just cleaning the data a bit.
Know your data, know your algorithms and always question the results.
That isn't to say the algorithm can not be used. It just is not bullet proof.