  Scientists in many fields have the common and basic need of dimensionality
reduction: visualizing the underlying structure of the massive multivariate
data in a low-dimensional space. However, many dimensionality reduction methods
confront the so-called "crowding problem" that clusters tend to overlap with
each other in the embedding. Previously, researchers expect to avoid that
problem and seek to make clusters maximally separated in the embedding.
However, the proposed in-tree (IT) based method, called IT-map, allows clusters
in the embedding to be locally overlapped, while seeking to make them
distinguishable by some small yet key parts. IT-map provides a simple,
effective and novel solution to cluster-preserving mapping, which makes it
possible to cluster the original data points interactively and thus should be
of general meaning in science and engineering.
