k-nearest neighbors (k-NN) is a simple, yet powerful algorithm used for classification and regression tasks in machine learning. It works by finding the 'k' closest data points to a given input in a dataset and making predictions based on the majority class or average value of those neighbors. This technique is particularly useful for pattern discovery and anomaly detection, as it leverages the concept of proximity to identify similar instances and distinguish outliers from typical data patterns.
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