Unsupervised Learning

 Unsupervised Learning : It can be explained as , we train our model or machine with some algorithms once and there after the machine learn by its own through it's environment without any human interference.

In unsupervised learning, unlike linear or logistic regression we do clustering, where Clustering can be defined as we make groups of the data of the similar kind and the groups made are known as Clusters. There are two approaches to clustering :

  • Generative : Where we follow Joint Probability Distribution.
  • Segmentation : It do segments of the data.


How Clustering is represented and How it works ????


In the above diagram, we have our data represented by some data points in a plot. When  we apply clustering over this data, it gets changed to :

Major Clustering Approaches :

  • Partitioning Approach :  It constructs various partitions and then evaluate them by some criterion. Eg :  Minimizing sum of squared errors. It assumes simple cluster shapes. Typical methods are : K-means which is numeric , K-mediods which is categorical, CLARANS.

  • Density Based : It is based on connectivity and the density functions. Typical methods are : DBSCAN , OPTICS , DenClue.

  • Hierarchical Approach : In this the similar groups are grouped together in some manner and we get the Hierarchical approach. The common example of this is Dendrogram. It has two types :- Agglomerative which follows bottom-up approach and Divisive which follows top-down approach. Typical methods are : Diana, Agnes, BIRCH, ROCK, CAMELEON.

  • Model -Based : It approximates Joint Probability Distribution. Typical methods are : EM (Expectation Maximization), SOM, COBWED.
Some examples of Unsupervised Learning are :
  • Association Rule
  • Sequence Pattern
  • NLP (embedding)



 


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