sklearn.cluster

Sklearn.cluster is a module in the scikit-learn library that provides various clustering algorithms. Clustering is a technique of grouping similar data points together in such a way that data points in the same group are more similar to each other than to those in other groups. There are many applications of clustering, such as market segmentation, image segmentation, and anomaly detection.

Some of the clustering algorithms provided by sklearn.cluster are:

1. KMeans: It is a popular clustering algorithm that partitions the data into K clusters.

2. AgglomerativeClustering: It is a hierarchical clustering algorithm that starts with each data point as a separate cluster and merges them iteratively based on a linkage criterion.

3. DBSCAN: It is a density-based clustering algorithm that groups together dense regions of data points separated by areas of lower density.

4. SpectralClustering: It is a clustering algorithm that uses graph theory to group together data points that are connected by edges in a graph.

5. Birch: It is a clustering algorithm that incrementally builds a hierarchical clustering tree to cluster the data points.

Sklearn.cluster also provides various metrics to evaluate the quality of the clustering results, such as silhouette score, homogeneity score, completeness score, and adjusted mutual information score.