  Previously, we proposed a physically-inspired method to construct data points
into an effective in-tree (IT) structure, in which the underlying cluster
structure in the dataset is well revealed. Although there are some edges in the
IT structure requiring to be removed, such undesired edges are generally
distinguishable from other edges and thus are easy to be determined. For
instance, when the IT structures for the 2-dimensional (2D) datasets are
graphically presented, those undesired edges can be easily spotted and
interactively determined. However, in practice, there are many datasets that do
not lie in the 2D Euclidean space, thus their IT structures cannot be
graphically presented. But if we can effectively map those IT structures into a
visualized space in which the salient features of those undesired edges are
preserved, then the undesired edges in the IT structures can still be visually
determined in a visualization environment. Previously, this purpose was reached
by our method called IT-map. The outstanding advantage of IT-map is that
clusters can still be found even with the so-called crowding problem in the
embedding.
  In this paper, we propose another method, called IT-Dendrogram, to achieve
the same goal through an effective combination of the IT structure and the
single link hierarchical clustering (SLHC) method. Like IT-map, IT-Dendrogram
can also effectively represent the IT structures in a visualization
environment, whereas using another form, called the Dendrogram. IT-Dendrogram
can serve as another visualization method to determine the undesired edges in
the IT structures and thus benefit the IT-based clustering analysis. This was
demonstrated on several datasets with different shapes, dimensions, and
attributes. Unlike IT-map, IT-Dendrogram can always avoid the crowding problem,
which could help users make more reliable cluster analysis in certain problems.
