📍 Cluster :
A group of data is known as cluster.
📍 K-mean :
It is an algorithm to solve the problems of cluster.
The algorithm works as follows:
- First we initialize k points, called means, randomly.
- We categorize each item to its closest mean and we update the mean’s coordinates, which are the averages of the items categorized in that mean so far.
- We repeat the process for a given number of iterations and at the end, we have our clusters.
- This GIF shows that how the algorithm works exactly….👇👇
✨ Where can I apply k-means?
k-means can typically be applied to data that has a smaller number of dimensions, is numeric, and is continuous. think of a scenario in which you want to make groups of similar things from a randomly distributed collection of things; k-means is very suitable for such scenarios.
here is a list of ten interesting use cases for k-means.
1. Document classification
cluster documents in multiple categories based on tags, topics, and the content of the document. this is a very standard classification problem and k-means is a highly suitable algorithm for this purpose. the initial processing of the documents is needed to represent each document as a vector and uses term frequency to identify commonly used terms that help classify the document. the document vectors are then clustered to help identify similarity in document groups.
2. 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
3. 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.
4. Customer Segmentation
clustering helps marketers improve their customer base, work on target areas, and segment customers based on purchase history, interests, or activity monitoring. They use White Paper on how telecom providers can cluster pre-paid customers to identify patterns in terms of money spent in recharging, sending sms, and browsing the internet. the classification would help the company target specific clusters of customers for specific campaigns.
5. Insurance fraud detection
machine learning has a critical role to play in fraud detection and has numerous applications in automobile, healthcare, and insurance fraud detection. utilizing past historical data on fraudulent claims, it is possible to isolate new claims based on its proximity to clusters that indicate fraudulent patterns. since insurance fraud can potentially have a multi-million dollar impact on a company, the ability to detect frauds is crucial.
6. 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.
7. Call record detail analysis
a call detail record (cdr) is the 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. in this article , you will understand how you 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.
8. Automatic clustering of it alerts
large enterprise it infrastructure technology components such as network, storage, or database generate large volumes of alert messages. because alert messages potentially point to operational issues, they must be manually screened for prioritization for downstream processes. clustering of data can provide insight into categories of alerts and mean time to repair, and help in failure predictions.
Thank you for reaching here…!!!😊✨