More Clustering Techniques
If you completed the preceding activity, you must have realized that you had to use a more robust approach to determine the number of clusters. You dealt with high dimensional data for clustering and therefore the visual analysis of the clusters necessitated the use of PCA. The visual assessment approach and the elbow method from the inertia plot however did not agree very well. This difference can be explained by understanding that visualization using PCA loses a lot of information and therefore provides an incomplete picture. Realizing that, you used the learning from the elbow method as well as your business perspective to arrive at an optimal number of clusters.
Such a comprehensive approach that incorporates business constraints helps the data scientist create actionable and therefore valuable customer segments. With these techniques learned and this understanding created, let us look at more techniques for clustering that will make the data scientist...
