Clustering is a technique of unsupervised learning for statistical data which is used is used in many fields. In data science, clustering is used to gain deep insights from the unstructured data by understanding where the data points fall when a clustering algorithm is applied. Hierarchical clustering and K-means clustering are the most common algorithms data scientists use in typical scenarios.
Hierarchical clustering decreases the numbers data points by merging the closest ones, thereby creating a hierarchy of the clustered data points. K-means text clustering algorithm is used for minimizing the distance of data points from an average data point.
Let’s discuss the five most interesting real-time uses of various data clustering algorithms-
Ensuring Fraud Detection
Perhaps, all of us know that machine learning plays a crucial role in fraud detection. Also, it has incredible applications in healthcare, automobile, or insurance fraud detection. Take the example of identifying taxi driver behavior. Clustering algorithms analyze the GPS logos and group similar behaviors. Based on these groups, you can classify them into fraudulent and legit ones.
Furthermore, by using the past data on fraudulent claims, you can find out the new claims based on their similarity to the clusters that show fraudulent patterns. This amazing ability to detect frauds is the most useful application of the K-means clustering algorithm.
Analyzing Fantasy League Stat
Machine learning via text clustering algorithms also has a wonderful application in the sporting world. For example, suppose you want to create a fantasy football team and identify which players would perform best for your team.
If you have a little performance data, you can take advantage of unsupervised learning via K-means to identify the players based on the players’ stats. But, what does K mean in text? Well, K denotes the number of clusters found from the source database.
With this clustering, you can determine the similar players who have the qualities you want in your team players. So, you get a better team to play for you and beat the competition.
Helps In Marketing And Sales
If you run a business and want to get the best ROI, it is important to target the right audience the right way. Do it wrong, and it will affect the customers’ trust in your brand. Clustering algorithms can help you improve your customer base by segmenting the customers based on their interests, purchase history, and activities.
You can group together the people who share similar traits and those who will likely make a purchase. After you have segmented the audience into different groups, you can experiment with different marketing copies on each group, which helps you target them better next time.
Identifying Rumors Or Fake News
Every day, we see fake news and rumors on social media. With the advancement in social media features, this type of news is created and spread at an even faster rate. Luckily, clustering algorithms can help you filter the fake news from the content.
These algorithms take content from the article, check the words used, and then cluster them. The clusters help determine which news are genuine and which are fake. It is based on the fact that if a piece of news contains certain terms or words, it increases the chances of the news being fake.
Clustering The IT Alerts Automatically
The IT infrastructure components, like storage, database, etc., produce volumes of alert messages. These messages should be screened to find out which ones to prioritize that might point to an operational issue in the future. Data clustering algorithms can provide you with insights into alerts, giving you ample time to fix the issues or predict future problems.
These are just a handful of clustering algorithms uses in different fields. They help you understand what the data groups are pointing towards so that you can predict the possible future outcomes and take necessary action on time