1. Motivation (why is the K-means classification needed)

2. How is the K-means classification calculated

3. Determining the optimal number of centroids

4. Interpretation of K-means classification results

5. Example(s) of use of K-means classification

6. Advantages and disadvantages of K-means classification

7. In what situations is K-means classification a suitable method

8. In what situations is K-means classification not a suitable method

9. Alternatives to K-means classification