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Straightforward implementation of k-means clustering. Not all of the Rodinia data sets are present here, because they are fairly large.

There are some changes compared to Rodinia:

  1. The threshold value is integral instead of floating-point. This reflects how it is actually used, which is to indicate the lowest number of cluster reassignments we permit in an iteration before we finish the convergence loops.

  2. There is a maximum number of iterations (this is also present in some Rodinia implementations).

Both of the above values are set on a per-dataset basis.

The implementation outputs two values:

  1. The cluster centres.

  2. The number of iterations done by the convergence loop.

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