Scipy

SciPy is a free and open-source library that is built using NumPy as a foundation. The library offers a wide range of functions that are useful for scientific computing and data analysis. It has various modules that can be used for optimization, linear algebra, statistics, image processing, signal processing, and much more. With its modular design, you can easily integrate the functions of SciPy into your data analysis workflows.

It can be installed by running the command given below:

pip install scipy

It has dedicated packages for the process of clustering. There are two modules that can offer clustering methods. 

1. cluster.vq
2. cluster.hierarchy

cluster.vq 

This module gives the feature of vector quantization to use with the K-Means clustering in Python method. The quantization of vectors plays a major role in reducing the distortion and improving the accuracy. Mostly the distortion here is calculated using the Euclidean distance between the centroid and each vector. Based on this the vector of data points are assigned to a cluster.

cluster.hierarchy

This module provides methods for general hierarchical clustering and its types such as agglomerative clustering. It has various routines that can be used for applying statistical methods on the hierarchies, visualizing the clusters, plotting the clusters, checking linkages in the clusters, and also checking whether two different hierarchies are equivalent.

In this article, cluster.vq module will be used to carry out the K-Means clustering.

K-meansPrerequisite: K-means clustering

K-means clustering in Python is one of the most widely used unsupervised machine-learning techniques for data segmentation and pattern discovery. This article will explore K-means clustering in Python using the powerful SciPy library. With a step-by-step approach, we will cover the fundamentals, implementation, and interpretation of K-Means clustering, providing you with a comprehensive understanding of this essential data analysis technique.