What is k-means Clustering?

For further discussion of the use of the algorithm to be more understandable, I think it is necessary to focus on the algorithm itself. K average clustering is the clustering of unlabeled data by a common attribute due to the constant K.

Check this quick review of K-means clustering theory. If you wish to know how to implement it in Machine Learning, check this post.

The algorithm itself looks like this:

  1. Determine the Centroid
  2. Calculating the distance using Euclidean distance. from each point on the plane to each Centroid,
  3. We are looking for those points for each closest Centroid, and thus we relate each point to the Centroid.
  4. Take all the points that belong to the first Centroid and take the middle point between them, then repeat step 2.

Now let’s move on to where this algorithm can be successfully used.

K means clustering use cases

Source: askpython.com

1. Segmentation of the market

The market has its peculiarities, so it is vital to understand it. The ability to evaluate consumers’ desires is the basis of marketing. Fortunately, clustering can become a crucial assistant here. This algorithm makes it possible to determine consumer segments based on age, gender, place of residence, or other characteristics. Marketers can use this data to improve their strategies to offer a more desirable product for each segment.

2. Segmentation of the image

Sometimes it is essential to divide the image into parts. In this case, the user can use this algorithm to classify objects in the picture. The algorithm divides the image into several details, and the detail corresponds to a part or object of the image.

3. Anomaly identification

In any case, it is necessary to be able to find data that does not fit into the general concept. Detecting abnormal data can be key to building the pattern. In this case, K means clustering using the distances of points from the Centroid can determine the anomaly. If the point is too far away, it is considered a deviation.

4. Compression for Rendering

When working with data, it often becomes difficult to understand them because of visualization problems. And problems with visualization may arise, for example, due to the multidimensionality of data. Users can solve it thanks to the k-means algorithm, which allows you to assemble a multidimensional point under one Centroid.

5. Academic performance

In any academic institution, it is necessary to evaluate the student’s progress. The algorithm can group students with the same grades or exam scores. It will assess the academic level of the facility and students and create a plan for future development based on the results.

6. Document clusterization

Source: stackoverflow.com

When working with a lot of documents, you can’t do without clustering them. The k-means, using intelligent exploration, can group documents by classes based on their content.

7. Gene exploration

We all know how important the study of genes is. The algorithm enables you to separate them based on their sample features. It enables you to group them based on their expression, which helps to understand biological processes.

8. Medical help

Another part of biology is that an algorithm can play a vital role in solving health problems. Hospitals have numerous patients with various symptoms. The algorithm also allows you to classify the symptoms to find out in advance about the possible future illness of the patient. It significantly reduces the risk of a dangerous outcome and increases the chances for timely treatment.

9. Astronomy

Astronomical data analysis refers to the process of analyzing large amounts of data from astronomical observations to gain insight into celestial objects and phenomena. It may involve clustering or grouping stars, galaxies, or other celestial objects based on their properties, such as position, brightness, temperature, etc. K-Means clustering can be used to identify patterns and structures in the data. This information can help scientists understand the distribution and properties of astronomical objects.

The algorithm can also be implemented to identify groups of objects that have similar properties, such as stars in the same cluster of galaxies in the same group. Also, it can be valuable in making predictions about the evolution and behavior of these objects over time. This team is among the best if you need expert Python development services.

Benefits of K-means Clustering?

Source: inovex.de

The benefits of k-means clustering include:

  • Simplicity: k-means is quite simple to implement algorithms these days. This is one of the most popular choices for clustering problems.
  • Speed: it is computationally efficient and it can perfectly handle large datasets in a relatively short amount of time.
  • Scalability: k-means can be easily scaled to handle an increasing number of data points and dimensions.
  • Interpretability: k-means produces intuitive and easy-to-interpret cluster labels that can be used to gain insights into the structure of the data.
  • Reusability: the cluster labels produced by k-means can also be used as input features for other machine learning algorithms, making it a reusable tool in a data science workflow.
  • Versatility: k-means can be used for a variety of clustering tasks and can be adapted for several other types of data distributions.

All of its benefits we have shared with you in the discussion. You will find this thing more impressive and useful. All you need is to check online help and support to get know in depth about this process. All of the above-mentioned points we have shared with you are more than enough to use this option in different sectors. Machine learning is a highly efficient solution and it will never make you feel down by its selection ever. In the start, you need to get help and support from the professionals to avoid any type of inconvenience.


K means the algorithm is necessary for all the listed industries. Clustering objects by their features is one of the most significant parts of any business. Of course, I have not described all sectors where this algorithm can be applied. I just gave examples of its use. Whatever business you are engaged in, I am sure that the correct implementation of the k means will bring you huge benefits.