What is clustering in machine learning?

Clustering is one of the most popular techniques in unsupervised learning where data is grouped based on the similarity of the data points. The basic principle behind clustering is the assignment of a given set of observations into subgroups or clusters such that observations present in the same clusters have a degree of similarity. It is a method of unsupervised learning since there is no label attached to the data points. The machine has to learn the features and patterns all by itself without any given input-output mapping.

There are several clustering in Machine Learning, Some common clustering algorithms are

  • Centroid-based clustering: The first and foremost clustering algorithm, Centroid-based algorithm, is a non-hierarchical structure that allows data analysts to group data points in different clusters according to their attributes. K-Means Algorithm is the most popular centroid based clustering algorithm
  • Density-based clustering: These algorithms combine data inputs with high density into one cluster. Density-based algorithms look after the density of data inputs in a plot and thereby allocate them to clusters based on their proximity to each other.
  • Distribution-based Clustering: Distribution-based algorithms focus on grouping distinct data points based on their source of distribution. This is done by referring to Gaussian distributions.
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