Unsupervised Learning algorithm K-Means Clustering divides the unlabeled dataset into various clusters. K specifies the number of pre-defined clusters that must be created during the process; for example, if K=2, two clusters will be created, and if K=3, three clusters will be created, and so on.
When you have unlabeled data (data without defined categories or groups), K-means clustering is a sort of unsupervised learning that you can employ. Based on the attributes provided, the algorithm assigns each data point to one of K groups iteratively.
The kmeans technique is widely utilised in a wide range of applications, including market segmentation, document clustering, image segmentation, and compression, among others. When we do a cluster analysis, we normally want to achieve one of two things: Get a good sense of how the data we're dealing with is structured.
Many firms utilise cluster analysis to find consumers who are similar to one another so that they may modify the emails they send to them to maximise income. For example, a company might gather the following information about its customers: The percentage of emails that were opened. Per email, the number of clicks.
Unsupervised learning algorithm K-Means clustering Unlike supervised learning, there is no labelled data for this grouping. K-Means divides things into clusters based on their similarities and differences with objects in other groups.
