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In summary, in this module we covered two very important and
widely used algorithms, k-means and MapReduce.
We started the module by presenting the goal of clustering.
And since then, we've motivated many applications of clustering.
And then we dug into k-means as a specific algorithm for performing clustering.
And it really is the mostly widely used algorithm out there.
And then we took k-means as an opportunity to present MapReduce,
which is a general framework for parallelizing algorithms.
And we showed how you can apply an iterative version of MapReduce
to parallelize k-means.
And like we almost always say, what you learned in this module
really generalizes to a wide range of different applications, and
different things you might be interested in doing.
But it's really, really, really especially true for this module.
K-means is a very generic tool for clustering, and clustering is used in so
many different areas.
And MapReduce is a very, very powerful and widely used framework for
creating distributed implementations or parallelizing algorithms.
So, in summary,
here are the set of things that you should be able to do after finishing this module.
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