Summary
This chapter focused on text clustering, intending to segregate samples with distinct characteristics and assign them to different groups. Clustering is one of the most important areas in data science simply because most datasets come unlabeled. Here, we tried to provide a good overview of the topic, but in reality, we only scratched the tip of this gigantic iceberg.
In this context, we presented both hard and soft clustering methods to categorize speech-to-text transcriptions. Specifically, speech recognition, often coupled with the techniques presented in this book, provides a convenient way to gather text data. Finally, we presented methods that allow the automatic configuration of the clustering algorithms, along with metrics, to assess their performance.
We have finally reached the end of the book! But stay tuned, as much more excitement is waiting for the years to come!