Cluster analysis

task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters)

thumb|Dendrogram of a hierarchical clustering (UPGMA) with the height of the nodes (adapted from bacterial 5S rRNA sequence data). thumb|Dendrogram output for hierarchical clustering of marine provinces using presence / absence of sponge species. thumb|A dendrogram of the Tree of Life. This phylogenetic tree is adapted from Woese et al. rRNA analysis. The vertical line at bottom represents the [[last universal common ancestor (LUCA).]] thumb|Heatmap of RNA-Seq data showing two dendrograms in the left and top margins.

The tendency to erroneously consider the inevitable streaks or clusters arising in small samples from random distributions to be non-random

statistical concept

method in cluster analysis

embedding of data within a manifold based on a similarity function

Biclustering, block clustering,

Medoids are representative objects of a data set or a cluster within a data set whose sum of dissimilarities to all the objects in the cluster is minimal. Medoids are similar in concept to means or centroids, but medoids are always restricted to be members of the data set. Medoids are most commonly used on data when a mean or centroid cannot be defined, such as graphs. They are also used in contexts where the centroid is not representative of the dataset like in images, 3-D trajectories and gene expression (where while the data is sparse the medoid need not be). These are also of interest whil