Summary
The project in this chapter was about finding hidden similarity underneath newsgroups data, be it semantic groups, themes, or word clouds. We started with what unsupervised learning does and the typical types of unsupervised learning algorithms. We then introduced unsupervised learning clustering and studied a popular clustering algorithm, k-means, in detail.
We also talked about tf-idf as a more efficient feature extraction tool for text data. After that, we performed k-means clustering on the newsgroups data and obtained four meaningful clusters. After examining the key terms in each resulting cluster, we went straight to extracting representative terms among original documents using topic modeling techniques. Two powerful topic modeling approaches, NMF and LDA, were discussed and implemented. Finally, we had some fun interpreting the topics we obtained from both methods.
Hitherto, we have covered all the main categories of unsupervised learning, including dimensionality...
