Clustering is the task of dividing the population or data points into a number of groups consisting of similar data points. Simply, the aim is to segregate groups with similar traits and assign them into clusters. the goal of the k-means algorithm is to find groups in the data, with the number of groups represented by the variable k.
Below are the few use cases of k-means clustering;
Acedemic performance: Based on the scores, students are categorized into grades like A, B, or C.
Diagnostic systems: The medical profession uses k-means in creating smarter medical decision support systems, especially in the treatment of liver ailments.
Search engines: Clustering forms a backbone of search engines. When a search is performed, the search results need to be grouped, and the search engines very often use clustering to do this.
Delivery Store Optimization: Optimize the process of good delivery using truck drones by using a combination of k-means to find the optimal number of launch locations and a genetic algorithm to solve the truck route as a traveling salesman problem.
Identifying Crime Localities: With data related to crimes available in specific localities in a city, the category of crime, the area of the crime, and the association between the two can give quality insight into crime-prone areas within a city or a locality.
Fantasy league stats analysis: Analyzing player stats has always been a critical element of the sporting world, and with increasing competition, machine learning has a critical role to play here. as an interesting exercise, if you would like to create a fantasy draft team and like to identify similar players based on player stats, k-means can be a useful option.
Cyber Profiling Criminals: Cyber profiling is the process of collecting data from individuals and groups to identify significant co-relations. the idea of cyber profiling is derived from criminal profiles, which provide information on the investigation division to classify the types of criminals who were at the crime scene.
Insurance fraud detection: Utilizing past historical data on fraudulent claims, it is possible to isolate new claims based on their proximity to clusters that state fraudulent patterns. Since insurance fraud can have a multi-million dollar impact on a company, the ability to detect frauds is crucial.
Call record detail analysis: A call detail record is a piece of information captured by telecom companies during the call, SMS, and internet activity of a customer. this information provides greater insights about the customer’s needs when used with customer demographics. We can cluster customer activities for 24 hours by using the unsupervised k-means clustering algorithm. It is used to understand segments of customers with respect to their usage by hours.
There are many more cases where k-means is used but I let's hope this helps.
