In the previous chapter, we apply t-SNE to visualize the newsgroup text data in reduced 2 dimensions. T-SNE, or dimensionality reduction in general, is a type of unsupervised learning. Instead of having a teacher educating what particular output to produce, be it a class or membership (classification), be it a continuous value (regression), unsupervised learning identifies inherent structures or commonalities in the input data. Since there is no guidance in unsupervised learning, there is no clear answer on what is a right or wrong result. Unsupervised learning has the freedom to discover hidden information underneath input data.

An easy way to understand unsupervised learning is to think of going through many practice questions for an exam. In supervised learning, you are given answers to those practice questions. You basically figure out the relationship between the questions and answers and learn how to map the questions to the answers. Hopefully, you will do well in the actual exam in the end by giving the correct answers. However, in unsupervised learning, you are not provided with the answers to those practice questions. What you might do in this instance could include the following:

• Grouping similar practice questions so that you can later study related questions together at one time
• Finding questions that are highly repetitive so that you will not waste time on those
• Spotting rare questions so that you can be better prepared for them
• Extracting the key chunk of each question by removing boilerplate so you can cut to the point

You will notice that the outcomes of all these tasks are pretty open-ended. They are correct as long as they are able to describe the commonality, the structure underneath the data.

Practice questions are the features in machine learning, which are also often called attributes, observations, or predictive variables. Answers to questions are the labels in machine learning, which are also called targets or target variables. Practice questions with answers provided are labeled data. On the contrary, practice questions without answers are unlabeled data. Unsupervised learning works with unlabeled data and acts on that information without guidance.

Unsupervised learning can include the following types:

• Clustering: This means grouping data based on commonality, which is often used for exploratory data analysis. Grouping similar practice questions mentioned earlier is an example of clustering. Clustering techniques are widely used in customer segmentation or for grouping similar online behaviors for a marketing campaign.
• Association: This explores the co-occurrence of particular values of two or more features. Outlier detection (also called anomaly detection) is a typical case, where rare observations are identified. Spotting rare questions in the preceding example can be solved using outlier detection techniques.
• Projection: This maps the original feature space to a reduced dimensional space retaining or extracting a set of principal variables. Extracting the key chunk of practice questions is an example projection, or specifically a dimensionality reduction.

Unsupervised learning is extensively employed in the area of NLP mainly because of the difficulty of obtaining labeled text data. Unlike numerical data, such as house and stock data, online click streams, labeling text can sometimes be subjective, manual, and tedious. Unsupervised learning algorithms that do not require labels become effective when it comes to mining text data. In chapter 2 Exploring the 20 Newsgroups Dataset with Text Analysis Techniques, we have experienced using t-SNE to reduce dimensionality of text data. Now, let's explore text mining with clustering algorithms and topic modeling techniques. We start with clustering the newsgroups data.