Advantages and Disadvantages of K-means Clustering

Advantages of K-means Clustering:

1. K-means clustering is a very simple and fast algorithm.

2. It can efficiently deal with very large data sets.

3. It can warm-start the positions of centroids.

4. Generalizes to clusters of different shapes and sizes.

5. Guarantees convergence

Disadvantagess of K-means Clustering:

1. It assumes prior knowledge of the data and requires the analyst to choose the appropriate number of clusters (k) in advance.

2. The final results obtained are sensitive to the initial random selection of cluster centers.

3. It’s sensitive to outliers.

4. If you rearrange your data, it’s very possible that you’ll get a different solution every time you change the ordering of your data.