Clustering - Recap

Key Takeaways

The key takeaways from this section include:

• There are two main types of clustering algorithms: non-hierarchical clustering (k-means) and hierarchical agglomerative clustering
• You can quantify the performance of a clustering algorithm using metrics such as variance ratios
• When working with the k-means clustering algorithm, it is useful to create elbow plots to find an optimal value for $k$
• When using hierarchical agglomerative clustering, different linkage criteria can be used to determine which clusters should be merged and at what point
• Dendrograms and clustergrams are very useful visual tools in hierarchical agglomerative clustering
• Advantages of k-means clustering include easy implementation and speed, whereas the main disadvantage is that it isn't always straightforward how to pick the "right" value for $k$
• Advantages of hierarchical agglomerative clustering include easy visualization and intuitiveness, whereas the main disadvantage is that the result is very distance-metric-dependent
• You can use supervised and unsupervised learning together in a few different ways. Applications of this are look-alike models in market segmentation and semi-supervised learning