Clustering Algorithms

Clustering algorithms analyze similarities and differences within a dataset to organize observations into distinct groups, known as clusters. Data points within the same cluster are generally more similar to each other than to points in other clusters. Common approaches include K-means, hierarchical clustering, and density-based methods such as DBSCAN.

Clustering helps uncover hidden structures and relationships within unlabeled data. It allows organizations to explore large datasets, identify meaningful groups, detect unusual patterns, and generate insights without manually defining categories beforehand.