Run the algorithm on the given data sets. You may assume these data form compact clusters, so use point representatives in your algorithms if representatives are required in your choice of proximity measure. The data will be two-dimensional, so you may visually inspect the results of the clusterings. You may choose any appropriate proximity measure for your algorithm, and are only required to use one. No matter which algorithm you use, you should also report timing information. Given the amount of time taken and ``goodness'' of the best cluster, do you feel that your algorithm was a good choice for the given data sets? Describe all your results in a detailed report.
The number of extra points you will receive depends on the complexity of the algorithm implemented, the thoroughness of your experiments, and the quality of your report. The maximum number of points is 20 per implementation.