Machine Learning

DBSCAN

A density-based clustering algorithm that can identify noise and irregular clusters.

DBSCAN groups points that have enough neighbouring observations within a chosen distance. Dense regions become clusters, while isolated observations are marked as noise or outliers.

Unlike K-Means, DBSCAN does not require the number of clusters to be specified in advance and can discover irregular shapes. Its results are sensitive to the distance threshold and minimum-neighbour parameters.

Also in Machine Learning

Supervised Learning Unsupervised Learning K-Means K-Nearest Neighbours Logistic Regression Linear Regression

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