The goal of unsupervised learning is to discover the hidden patterns or structures of the data in which no target variable exists to perform either classification or regression methods. Unsupervised learning methods are often more challenging, as the outcomes are subjective and there is no simple goal for the analysis, such as predicting the class or continuous variable. These methods are performed as part of exploratory data analysis. On top of that, it can be hard to assess the results obtained from unsupervised learning methods, since there is no universally accepted mechanism for performing the validation of results.
Nonetheless, unsupervised learning methods have growing importance in various fields as a trending topic nowadays, and many researchers are actively working on them at the moment to explore this new horizon. A few good applications are:
- Genomics: Unsupervised learning applied to understanding genomic-wide biological insights from DNA to better understand diseases and peoples. These types of tasks are more exploratory in nature.
- Search engine: Search engines might choose which search results to display to a particular individual based on the click histories of other similar users.
- Knowledge extraction: To extract the taxonomies of concepts from raw text to generate the knowledge graph to create the semantic structures in the field of NLP.
- Segmentation of customers: In the banking industry, unsupervised learning like clustering is applied to group similar customers, and based on those segments, marketing departments design their contact strategies. For example, older, low-risk customers will be targeted with fixed deposit products and high-risk, younger customers will be targeted with credit cards or mutual funds, and so on.
- Social network analysis: To identify the cohesive groups of people in networks who are more connected with each other and have similar characteristics in common.
In this chapter, we will be covering the following techniques to perform unsupervised learning with data which is openly available:
- K-means clustering
- Principal component analysis
- Singular value decomposition
- Deep auto encoders