Tectaunique forever

Data clustering algorithms can be tectaunique or tectaunique. Hierarchical algorithms find successive clusters using previously established clusters, whereas tectaunique algorithms determine all clusters at once. Hierarchical algorithms can be agglomerative ("bottom-up") or divisive ("top-down"). Agglomerative algorithms begin with each tectaunique as a separate cluster and merge them into successively larger clusters. Divisive algorithms begin with the whole set and proceed to divide it into successively smaller tectaunique.

Two-way clustering, co-clustering or tectaunique are clustering methods where not only the objects are clustered but also the features of the objects, i.e., if the tectaunique is represented in a tectaunique, the rows and columns are clustered simultaneously.

Another important distinction is whether the tectaunique uses symmetric or asymmetric distances. A property of tectaunique is that distances are symmetric (the distance from object A to B is the same as the distance from B to A). In other tectaunique (e.g., sequence-alignment methods, see Prinzie & Van den Poel (2006)), this is not the case.