Cluster analysis is a method used to group objects based on their similarities. There are two main types of algorithms: agglomerative and partitional. Agglomerative algorithms merge similar clusters, while partitional algorithms create distinct groups. The K-means method is commonly used for partitional clustering. Other techniques like DBSCAN and BIRCH focus on clustering data based on density in Euclidean spaces. DBSCAN relies on density reachability and density connectability, while BIRCH is efficient for clustering in vector spaces.