The data, before and after k-means clustering, is shown in Figure 2. Notice that there is no inherent best clustering of data. The results look reasonable but many other clusterings are possible. The ...

Compared to other clustering techniques, k-means is simple and works well with large datasets. The main weakness of k-means is that it assumes clusters are circular / spherical in shape, and therefore ...