K-Means Clustering-Use Cases

Let's first understand what is Clustering…?


Clustering is one of the most common exploratory data analysis technique, used to get an intuition about the structure of the data.

It can be defined as the task of identifying subgroups in the data such that data points in the same subgroup (cluster) are very similar while data points in different clusters are very different. In other words, we try to find homogeneous subgroups within the data such that data points in each cluster are as similar as possible according to a similarity measure such as euclidean-based distance or correlation-based distance.

Clustering analysis can be done on the basis of features where we try to find subgroups of samples based on features. Clustering is used in market segmentation; where we try to find customers that are similar to each other whether in terms of behaviors or attributes, image segmentation/compression; where we try to group similar regions together, document clustering based on topics, etc.

Clustering is considered an unsupervised learning method since we don’t have the ground truth to compare the output of the clustering algorithm to the true labels to evaluate its performance. We only want to try to investigate the structure of the data by grouping the data points into distinct subgroups.


K-means algorithm is an iterative algorithm that tries to partition the dataset into Kpre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group. It tries to make the intra-cluster data points as similar as possible while also keeping the clusters as different (far) as possible. It assigns data points to a cluster such that the sum of the squared distance between the data points and the cluster’s centroid (arithmetic mean of all the data points that belong to that cluster) is at the minimum. The less variation we have within clusters, the more homogeneous (similar) the data points are within the same cluster.

The K-means algorithm works as follows:

  1. Specify the number of clusters K.
  2. Initialize centroids by first shuffling the dataset and then randomly selecting K data points for the centroids without replacement.
  3. Keep iterating until there is no change to the centroids. i.e the assignment of data points to clusters isn’t changing.
  4. Compute the sum of the squared distance between data points and all centroids.
  5. Assign each data point to the closest cluster (centroid).
  6. Compute the centroids for the clusters by taking the average of all data points that belong to each cluster.


K-means algorithm is very popular and used in a variety of applications such as market segmentation, document clustering, image segmentation and image compression, etc. The goal usually when we undergo a cluster analysis is either:

  1. Get a meaningful intuition of the structure of the data we’re dealing with.
  2. Cluster-then-predict where different models will be built for different subgroups if we believe there is a wide variation in the behaviors of different subgroups. An example of that is clustering patients into different subgroups and build a model for each subgroup to predict the probability of the risk of having a heart attack.

Let's see some Applications of K-means algorithm in real world…!

1. Identifying Fake News

Fake news is not a new phenomenon, but it is one that is becoming prolific.Fake news is being created and spread at a rapid rate due to technology innovations such as social media.

The K-means algorithm works by taking in the content of the fake news article, the corpus, examining the words used and then clustering them. These clusters are what helps the algorithm determine which pieces are genuine and which are fake news. Certain words are found more commonly in sensationalized, click-bait articles. When you see a high percentage of specific terms in an article, it gives a higher probability of the material being fake news.

2. Spam filter

Spam emails are at best an annoying part of modern day marketing techniques, and at worst, an example of people phishing for your personal data. To avoid getting these emails in your main inbox, email companies use algorithms. The purpose of these algorithms is to flag an email as spam correctly or not.

K-Means clustering techniques have proven to be an effective way of identifying spam. The way that it works is by looking at the different sections of the email (header, sender, and content). The data is then grouped together. These groups can then be classified to identify which are spam. Including clustering in the classification process improves the accuracy of the filter to 97%. This is excellent news for people who want to be sure they’re not missing out on your favorite newsletters and offers.

3. Classifying Network Traffic

Imagine you want to understand the different types of traffic coming to your website. You are particularly interested in understanding which traffic is spam or coming from bots.

As more and more services begin to use APIs on your application, or as your website grows, it is important you know where the traffic is coming from. For example, you want to be able to block harmful traffic and double down on areas driving growth. However, it is hard to know which is which when it comes to classifying the traffic.

K-means clustering is used to group together characteristics of the traffic sources. When the clusters are created, you can then classify the traffic types. The process is faster and more accurate than the previous Autoclass method. By having precise information on traffic sources, you are able to grow your site and plan capacity effectively.

4. Identifying fraudulent or criminal activity

In this scenario, we are going to focus on fraudulent taxi driver behavior. However, the technique has been used in multiple scenarios.You need to look into fraudulent driving activity. The challenge is how do you identify what is true and which is false?

By analysing the GPS logs, the algorithm is able to group similar behaviors. Based on the characteristics of the groups you are then able to classify them into those that are real and which are fraudulent.

So, From this article We got to know that how the K-means clustering algorithm is helping the Security Domain in real world.!

Thanks For Reading😇

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