Four methods for clustering data in Python

Clustering is the division of objects into subsets (clusters) according to a given criterion. Each cluster includes objects as similar to each other as possible. Imagine moving to a new house; to do so, you need to sort all your belongings into categories; this can be winter/summer clothes, silverware, fragile objects, etc. This sorting process makes it easier to understand how much stuff you have to move and maybe even helps to get rid of unnecessary things. Clustering does the same thing but with data.

Analyzing sorted information is much easier.

A person, not an algorithm, determines the criteria for clustering – that’s how it differs from classification. However, there are situations when the two are used simultaneously. The Random Forest algorithm is one of the few universal classification methods often paired up with clustering. And working with a large amount of data, the result is imposing, especially as the time/value/resources compromise.

Today, we will look at the most popular Python clustering methods.


The K-means analysis is one of the simplest and most common methods. It aims to distribute the data points among the k-clusters so that the distances from each moment to the respective centroid are minimized.

In simple terms, the K-means analysis groups similar data points together, allowing to identify of the underlying patterns. It is a partitioning method that is particularly suitable for large data sets.

Hierarchical clustering

Hierarchical clustering is a distance-based approach to creating clusters. There are two ways hierarchical clustering is done:

  1. Divisive Clustering;
  2. Agglomerative Clustering.

These are complicated words, but there are simple concepts behind them. In divisive clustering, all elements form a cluster divided into sub-clusters. This method reduces heterogeneity.

Agglomerative clustering is the exact opposite. Each element forms a cluster, which then includes larger clusters. The method increases the heterogeneity as more details are getting added.


t-SNE is a machine learning algorithm used to visualize a multidimensional dataset as multidimensional shapes. The abbreviation stands for “t-distributed stochastic neighborhood embedding.”

The most crucial feature of t-SNE – it can be used to reduce the dimension of the dataset to preserve internal relationships.

There are a lot of visualization tools and libraries that are used to implement t-SNE using Python.


Density-Based Spatial Clustering of Applications with Noise, DBSCAN for short, is an unsupervised learning method in which we group observation points based on specific characteristics into clusters of arbitrary shape.

It not only can correctly distinguish clusters of complex shapes but is also excellent at detecting noise. This density-based algorithm assumes that clusters are dense areas in space, separated by “emptiness.”It gathers the “densely clustered” data points into a single place.

Additionally, DBSCAN clustering is resistant to “outliers” and does not require specifying the number of clusters in advance.


Clustering aims to identify groups of data with similar features and determine the correspondence between them. Initially, we do not have information on such clustering, so our task is to use all available data to predict the post of sample items to their clusters.

Clustering has been formulated in one form or another in such scientific fields as statistics, pattern recognition, optimization, and machine learning. Hence, there are many synonyms for the notion of a cluster – taxon and densification for example.

Currently, the number of methods for partitioning groups of objects into clusters is quite large – several dozens of algorithms and even more modifications.