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...