K-means clustering is an unsupervised learning method for categorizing unlabeled data by grouping them based on their features instead of pre-defined categories. Here K is a variable that shows the number of categories generated. The main objective is to classify the information into K clusters and detail each cluster’s center of mass. Then using the closed center of mass, a cluster (class) can be assigned to a new data point. The significant benefit of this method is that it eliminates the bias that comes from people. Instead of a researcher categorizing things into groups for classification, the machine does it based on facts, not guesses.
Applications of K-means
Image Segmentation
Image segmentation divides an image into different segments based on similarities like colors or textures. The image pixels are grouped based on their colors using K-means clustering. This is useful for object detection, in which objects in a picture can be extracted after segmentation so they can be studied further.
Customer Segmentation
K-means can be utilized to divide customers into groups based on their buying habits, demographic information, and other factors. This lets companies figure out the different kinds of customers they have and send them marketing messages that are more relevant to them. For example, a store can utilize customer segmentation to find customers more likely to buy high-end products and send them personalized offers.
Fraud Detection
K-means can be utilized to find fraud by grouping transactions based on their similarities. This can assist banking organizations in finding unusual things and stopping fraud.
Anomaly Detection
K-means can be utilized to group data points together based on their similarities and find data points that don’t fit into any group. For example, K-means can be utilized in a manufacturing facility to find oddities in sensor data, which could mean that a machine isn’t working right.
Recommender Systems
K-means can be utilized in recommender systems to team users or items based on their similarities and make suggestions to users. For example, on a platform for streaming movies, K-means may be utilized to group users who like the same kinds of films and suggest movies based on what this group prefers. In the same way, K-means may be applied to group films based on their similarities and recommend similar movies to people who have already watched one movie.
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