What is UnSupervised Learning Algorithm
In Unsupervised learning, there is no training dataset with the correct answers. Instead we let the system load all the data and find structure / patterns in the data on its own. So essentially the machine eats all the data, visualizes it and find groups / clusters in the data
For example: when you visit news.google.com , All the news articles from different sources (news outlets) are being grouped into one based on the similarity, that is an example for clustering problem
We can derive this structure by clustering the data based on relationships among the variables in the data
Types of UnSupervised Algorithm
Clustering
A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior
Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on
Given a large dataset of medical records from patients suffering from heart disease, try to learn whether there might be different clusters of such patients for which we might tailor separate treatments.
Association
An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y
Non-clustering
The "Cocktail Party Algorithm", allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party
Segmentation:
Given data on how 1000 medical patients respond to an experimental drug (such as effectiveness of the treatment, side effects, etc.), discover whether there are different categories or "types" of patients in terms of how they respond to the drug, and if so what these categories are