Clusters
In data science, clusters refer to groups of data points that share similar characteristics or patterns. Clustering is a technique used in unsupervised learning to identify these groups and assign data points to them based on their similarity. The goal of clustering is to find meaningful patterns in the data that can be used for further analysis or decision-making. Clustering algorithms can be hierarchical or non-hierarchical, and can use different distance metrics to measure similarity between data points. Some common applications of clustering include customer segmentation, anomaly detection, and image segmentation.
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