You have an Anomaly Detection project you’re working on and you need a simple approach...

Clustering is defined as “an unsupervised process that partitions data into groups (clusters) based on similarity without preassigned labels.” K-Means is the canonical unsupervised clustering algorithm, iteratively assigning points to K centroids to minimize within-cluster variance. This makes K-Means the simplest and most direct choice for grouping data in an anomaly-detection context.

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