K-Means Clustering Algorithm

Review the "k-MEANS CLUSTERING ALGORITHM" section in Chapter 4 of the Sharda et. al. textbook for additional background.

Use Excel to perform the following data analysis.

1. Plot the data on a scatter plot.
2. Determine the ideal number of clusters.
3. Choose random center points (centroids) for each cluster. (Note:  Each student will select a different random set of centroids.)
4. Using a standard distance formula measure the distance from each data point to each center point.
5. Assign each data point to an initial cluster region based on closeness.
6. For each cluster calculate new center points.
7. Repeat steps 4 through 6.