1.

What is clustering? What are the different types? Briefly explain.

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Response: 

CLUSTERING is part of unsupervised learning in machine learning and data science. Cluster analysis or data segmentation is an exploratory method for identifying homogeneous groups or clusters of records. 

  • SIMILAR records should belong to the same cluster. 
  • Dissimilar records should belong to different clusters. 

Clustering algorithms are largely distinguished by two characteristics. ONE is "SIMILARITY metric" and the other is "agglomeration function (kind of merge/bottom up) strategy". 

Clustering can be of various types. Some key categories are as FOLLOWS: 

  1. Hierarchical clustering – using connectivity models 
  2. K-means clustering – using centroid models 
  3. Expectation-maximization – statistics based 
  4. Density-based – statistics based 


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