What is clustering in Machine Learning?

Clustering is an unsupervised learning approach that involves grouping data elements. The clustering technique may be used if you have a set of data points. This method will enable you to categorize all of the data points into their respective groupings. Data points belonging to the same category have comparable features and properties, whereas data points belonging to other groupings have diverse features and properties. You may use this approach to analyze statistical data. Let’s look at three of the most widely used and effective clustering techniques.

K-means clustering: When you have data that doesn’t fit into any single group or category, this approach is often employed. It enables you to uncover hidden patterns in data that may be used to categorize them into different categories. The number of groups they are split into is represented by the variable k, and the data points are clustered based on feature similarity. The cluster centroids are utilized to label fresh data in this case.

Mean-shift clustering: The main goal of this technique is to locate the center points of all the groups by updating the center point candidates to be the mean. Unlike k-means clustering, you don’t have to choose the number of groups because the mean shift will figure it out automatically.

Density-based spatial clustering of applications with noise (DBSCAN): This clustering is similar to mean-shift clustering in that it is based on density. There is no need to pre-set the number of clusters, but it does identify outliers and considers them as noise, unlike mean-shift. Furthermore, it is capable of detecting clusters of any size or form with little effort.

Clustering is an unsupervised learning algorithm. In unsupervised learning the aim is to understand the underlying structure of the data. Here it is to be kept in mind that the data doesn’t have any labels associated with it.

Clustering algorithms can be categorised into:

  • Connectivity based clustering
  • Centroid based clustering

An example of connectivity based clustering is hierarchical clustering and an example of centroid based clustering is k-means algorithm.

In hierarchical clustering, we create clusters using a dendogram. Dendogram is a tree like structure which would tell us exactly at what distance which data points were grouped into a cluster.

When it comes to k-means algorithm, it is based on floyd’s algorithm and that is why the centroids are changed each time.