Unsupervised learning in Machine learning

Unsupervised learning is learning from observation and discovery. In this mode of learning, there is no training set or prior knowledge of the classes. The system analyzes the given set of data to observe similarities emerging ou of the subsets of the data. The outcome is a set of class descriptions, one for each class, discovered in the environment. This is similar to cluster analysis in statistics.

Unsupervised learning makes sense of unlabeled data without having any predefined data set for its training. Unsupervised learning is an extremely powerful tool for analyzing available data and look for patterns and trends. It is most commonly used for clustering similar input into logical groups.

common approaches to unsupervised learning include- 

1. k-means 
2. Self-organizing maps
3. Hierarchical clustering

Some popular examples of unsupervised learning algorithm are-

1. Genetic algorithms 
2. Clustering approaches 
3. A priori algorithm for association Glue learning problems.