Summary
In this chapter, we've reviewed three techniques with a broad range of applications for preprocessing and dimensionality reduction. In doing so, you learned a lot about an unfamiliar dataset.
We started out by applying PCA, a widely-utilized dimensionality reduction technique, to help us understand and visualize a high-dimensional dataset. We then followed up by clustering the data using k-means clustering, identifying means of improving and measuring our k-means analysis through performance metrics, the elbow method, and cross-validation. We found that k-means on the digits dataset, taken as is, didn't deliver exceptional results. This was due to class overlap that we spotted through PCA. We overcame this weakness by applying PCA as a preprocess to improve our subsequent clustering results.
Finally, we developed an SOM algorithm that delivered a cleaner separation of the digit classes than PCA.
Having learned some key basics around unsupervised learning techniques and analytical methodology, let's dive into the use of some more powerful unsupervised learning algorithms.
