gives us a measure of the effectiveness of an IM by itself.

-   [**Final SSE**]{}: This is the SSE value calculated after the clustering phase. It gives us a measure of the effectiveness of an IM when its output is refined by k-means. Note that this is the objective function of the k-means algorithm, i.e., .

-   [**Number of Iterations**]{}: This is the number of iterations that k-means requires until reaching convergence when initialized by a particular IM. It is an efficiency measure independent of programming language, implementation style, compiler, and CPU architecture.

-   [**CPU Time**]{}: This is the total CPU time in milliseconds taken by the initialization and clustering phases.

All of the methods were implemented in the `C` language, compiled with the `gcc` v4.4.3 compiler, and executed on an Intel Xeon E5520 2.26GHz machine. Time measurements were performed using the `getrusage` function, which is capable of measuring CPU time to an accuracy of a microsecond. The MT19937 variant of the Mersenne Twister algorithm was used to generate high quality pseudorandom numbers [@Matsumoto98].

The convergence of k-means was controlled by the disjunction of two criteria: the number of iterations reaches a maximum of $100$ or the relative improvement in 