Explain What is clustering in Machine Learning?

Clustering is a technique used in unsupervised learning that involves grouping data points. If you have a set of data points, you can make use of the clustering algorithm. This technique will allow you to classify all the data points into their particular groups. The data points that are thrown into the same category have similar features and properties, whereas the data points that belong to different groups have distinct features and properties. This method allows you to perform statistical data analysis. Let’s take a look at three of the most popular and useful clustering algorithms.

  • K-means clustering: This algorithm is commonly used when you have data with no specific group or category. It allows you to find the hidden patterns in the data that can be used to classify them into various groups. The variable k is used to represent the number of groups they are divided into, and the data points are clustered using the similarity of features. Here, the centroids of the clusters are used for labeling new data.
  • Mean-shift clustering: The main aim of this algorithm is to update the center point candidates to be the mean and find the center points of all the groups. Unlike k-means clustering, in this, you do not need to select the possible number of clusters as it can automatically be discovered by the mean shift.
  • Density-based spatial clustering of applications with noise (DBSCAN): This clustering is based on density and has similarities with mean-shift clustering. There is no need to pre-set the number of clusters, but unlike mean-shift, it identifies outliers and treats them like noise. Moreover, it can identify arbitrarily sized and shaped clusters without much effort.