Types of Unsupervised Learning

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Unsupervised Learning is classified into two:


It is applied to group data based on different patterns, our machine model finds. Cluster analysis finds the commonalities between the data objects and categorizes them as per the presence and absence of those commonalities.


It is an unsupervised learning method that is used for finding the relationships between variables in a large database.

Types of Clustering

  1. Exclusive Clustering: In this method, data are grouped in such a way that one data can belong to one cluster only.
  2. Hierarchical Clustering: In this technique, every data is a cluster. The iterative unions between the two nearest clusters reduce the number of clusters.
  3. Overlapping Clustering: It uses fuzzy sets to cluster data. Here each point may belong to two or more clusters with separate degrees of membership.

Some more algorithms

  • K-Means Clustering
  • DBSCAN – Density-Based Spatial Clustering of Applications with Noise
  • BIRCH – Balanced Iterative Reducing and Clustering using Hierarchies
  • KNN (k-nearest neighbours)