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Feature Description
The K-Means algorithm is a widely used unsupervised learning technique for clustering data. The algorithm partitions the dataset into k clusters, where each cluster is defined by its centroid. The core idea is to minimize the within-cluster variance by iteratively assigning data points to the nearest centroid and updating the centroids based on the mean of the assigned points. This process continues until convergence, typically when the assignments no longer change or the centroids stabilize.
Use Case
A practical use case for the K-Means algorithm is in customer segmentation. For example, in a retail business, K-Means can group customers into distinct clusters based on their purchasing behavior and demographics. This segmentation allows businesses to tailor marketing strategies, optimize product offerings, and enhance customer experiences by understanding the unique characteristics and preferences of each customer group. By identifying distinct segments, K-Means helps in targeting promotions more effectively and improving overall customer satisfaction.
Priority
High
Record