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K-means

K-means is a clustering algorithm used in the field of unsupervised learning. The goal of the algorithm is to group a dataset into K clusters (where K is a predefined number of clusters) based on the similarity of the observations to each other. The algorithm initially assigns K centroids randomly and then assigns each data point to the nearest centroid. The centroids are recalculated repeatedly, and the points are reassigned to the nearest centroids until convergence is reached. It is widely used in tasks such as customer segmentation, text classification, and image processing.

Applications of the K-means Algorithm

  1. Customer Segmentation:Group customers based on common characteristics for targeted marketing.
  2. Image Compression:Reduce the number of colors in an image by representing similar colors with the same value.
  3. Document Clustering:Organize documents into groups based on their content and similarity.
  4. Pattern Analysis:Detect patterns in biometric data, such as fingerprints or genetic data.
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