Unsupervised Learning
Unlike supervised learning, the unsupervised learning process involves data that is neither classified nor labeled. The algorithm will perform analysis on the data without guidance. The job of the machine is to group unclustered information according to similarities in the data. The aim is for the model to spot patterns in the data in order to give some insight into what the data is telling us and to make predictions.
An example is taking a whole load of unlabeled customer data and using it to find patterns to cluster customers into different groups. Different products could then be marketed to the different groups for maximum profitability.
Unsupervised learning is broadly categorized into two types:
- Clustering: A clustering procedure helps to discover the inherent patterns in the data.
- Association: An association rule is a unique way to find patterns associated with a large amount of data, such as the supposition that when someone buys product 1, they also tend to buy product 2.
