Several clustering algorithms are commonly used in cancer research:
K-means Clustering: A simple and widely used algorithm that partitions data into k clusters. Hierarchical Clustering: Builds a hierarchy of clusters and is useful for understanding the relationships between clusters. DBSCAN: Density-Based Spatial Clustering of Applications with Noise; useful for identifying clusters of varying shapes and sizes. Gaussian Mixture Models: Assumes that the data is generated from a mixture of several Gaussian distributions.