Difference between regular clustering and k-means clustering

Question

I have a set of N values and I perform the following two operations on them

1) Sort and iterate and partition the values into k buckets.

2) Run the Lloyd's algorithm (as given here) and get k means.

Can someone please explain the physical significance and difference between the two results?

Answer 1

In #2 k means establishes a Voronoi diagram which provides partitions for classifying instances. You can think of each partition as a "bucket" for #1.

Your description of #1 doesn't really describe an algorithm so in a vague way there isn't a difference between the two descriptions.