In statistical analysis, various clustering techniques are available beyond the previously mentioned methods such as K-means and DBSCAN. Among these alternatives are hierarchical clustering, which constructs a dendrogram to represent clusters, and K-medoids, which employs the medoid as the cluster center for increased resistance to outliers. Fuzzy C-Means introduces the concept of fuzzy membership, allowing data points to belong to multiple clusters with varying degrees of membership.
Agglomerative Nesting (AGNES) is an agglomerative hierarchical approach that progressively merges clusters, starting with individual data points. OPTICS, similar to DBSCAN, is a density-based algorithm but utilizes a reachability plot to identify clusters with diverse shapes and densities. Affinity Propagation designates exemplars and assigns data points to these representatives
Spectral Clustering leverages eigenvalues for dimensionality reduction before clustering, effective for non-linear structures.
Mean Shift, a non-parametric algorithm, iteratively shifts points towards density function peaks. Self-Organizing Maps (SOM) is an artificial neural network method for clustering and visualizing high-dimensional data on a lower-dimensional grid. These diverse techniques offer a range of options for clustering based on data characteristics and analytical objectives.