All the presented algorithms have pros and cons. You have to understand the algorithms(tools) at your disposal and find the best for the questions you want to make.
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.
I am a recently graduated student and "future" software developer and I have been interested in the subject of static code analysis for the past few years.
Today I am asking all of us in order to get some insight about what we as programmers really do and want regarding static code analysis.
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.