k-mean clustering and its real usecase in the security domain

What Is K-Means Clustering?

Machine Learning is divided in four major categories.That are Supervised,Unsupervised ,semi-supervised reinforcement learning.The K-Means clustering is technique of unsupervised learning.An unsupervised learning is a technique, in which the system is not trained with human supervision. where as supervised learning involves feeding training data into your machine learning algorithm that includes labels.

Here are 7 examples of clustering algorithms in action.

1. Identifying Fake News

Fake news is not a new phenomenon, but it is one that is becoming prolific.

2. Spam filter

You know the junk folder in your email inbox? It is the place where emails that have been identified as spam by the algorithm.

3. Marketing and Sales

Personalization and targeting in marketing is big business.

4. 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.

5. 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.

6. Document analysis

There are many different reasons why you would want to run an analysis on a document. In this scenario, you want to be able to organize the documents quickly and efficiently.

7. Fantasy Football and Sports

Ok so up until this point we have looked into different business problems and how clustering algorithms have been applied to solve them.

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