information:filtering, structural:reformatting, linguistic:voice_change, structural:expansion
Connectivity-based clustering (hierarchical clustering)

These methods will not produce a unique partitioning of the data set, but a hierarchy from which the user still needs to choose appropriate clusters. They are not very robust towards outliers, which will either show up as additional clusters or even cause other clusters to merge (known as "chaining phenomenon", in particular with single-linkage clustering). In the general case, the complexity is O (n 3) {\displaystyle {\mathcal {O}}(n^{3})} for agglomerative clustering and O (2 n − 1) {\displaystyle {\mathcal {O}}(2^{n-1})} for divisive clustering, which makes them too slow for large data sets. For some special cases, optimal efficient methods (of complexity O (n 2) {\displaystyle {\mathcal {O}}(n^{2})}) are known: SLINK for single-linkage and CLINK for complete-linkage clustering.