Other clustering techniques-
Hierarchical Clustering:
Agglomerative Hierarchical Clustering: This method starts with individual data points as separate clusters and merges them based on similarity until one cluster is formed. The result is a tree-like structure or dendrogram.
Divisive Hierarchical Clustering: The opposite of agglomerative clustering, divisive hierarchical clustering starts with one cluster that includes all data points and recursively divides it into smaller clusters.
K-Medoids:
K-Medoids is similar to K-means but instead of using the mean as a center, it uses the medoid, which is the most centrally located point in a cluster. This makes K-medoids less sensitive to outliers than K-means.
Gaussian Mixture Model (GMM):
GMM assumes that the data is generated by a mixture of several Gaussian distributions. It is a probabilistic model that assigns a probability to each point belonging to a certain cluster, allowing for soft assignments.
OPTICS (Ordering Points To Identify the Clustering Structure):
OPTICS is a density-based clustering algorithm similar to DBSCAN but with a different approach to ordering points. It creates a reachability plot, which helps in identifying clusters of varying shapes and densities.