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What us Unsupervised Learning?


By NIIT Editorial

Published on 01/03/2021

6 minutes

Unsupervised learning refers to the procedure of training a machine learning algorithm with unlabelled data. The algorithm is given a free hand at understanding the key characteristics of the training data and then define the chief specifications of the same, its own. The code is supposed to do the heavy lifting of identifying patterns in the data. 

 

This exercise often churns surprising, and often intriguing results from the code. For instance, the machine could be served a picture of cats and dogs and asked to identify each. If it comes down to facial physiognomy, the code can identify that both the cats and the dogs have whiskers, two eyes and a mouth. However, if enough images populate the training set, the code would learn to draw the line between the two animals based on their skin patterns, canine size, ears so on and so forth. 

 

Just like supervised learning, unsupervised learning can be divided into two parts: 

 

Clustering - As the name suggests, this refers to such business cases where you want to cluster the training data according to a mutual characteristic. For example, a database of thousands of customers can be grouped as per the purchasing needs of each demographic. Clustering can further be divided into the following sub-categories: 

  • Exclusive (partitioning)
  • Agglomerative 
  • Overlapping 
  • Probabilistic 

 

Association - This is more complicated than clustering in that it goes a step forward and relates two sets of clusters with one another. For instance, customers who belong to a particular age group, purchase more of product A than product B. Association can be divided into the following types: 

 

  • Hierarchical learning 
  • K-means clustering 
  • Principal component analysis 
  • Singular value decomposition 
  • Independent component analysis 


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