Leading Tree in DPCLUS and Its Impact on Building Hierarchies
  This paper reveals the tree structure as an intermediate result of clustering
by fast search and find of density peaks (DPCLUS), and explores the power of
using this tree to perform hierarchical clustering. The array used to hold the
index of the nearest higher-densitied object for each object can be transformed
into a Leading Tree (LT), in which each parent node P leads its child nodes to
join the same cluster as P itself, and the child nodes are sorted by their
gamma values in descendant order to accelerate the disconnecting of root in
each subtree. There are two major advantages with the LT: One is dramatically
reducing the running time of assigning noncenter data points to their cluster
ID, because the assigning process is turned into just disconnecting the links
from each center to its parent. The other is that the tree model for
representing clusters is more informative. Because we can check which objects
are more likely to be selected as centers in finer grained clustering, or which
objects reach to its center via less jumps. Experiment results and analysis
show the effectiveness and efficiency of the assigning process with an LT.
