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This paper reveals the tree structure as an intermediate result of clusteringby fast search and find of density peaks (DPCLUS), and explores the power ofusing this tree to perform hierarchical clustering. The array used to hold theindex of the nearest higher-densitied object for each object can be transformedinto a Leading Tree (LT), in which each parent node P leads its child nodes tojoin the same cluster as P itself, and the child nodes are sorted by theirgamma values in descendant order to accelerate the disconnecting of root ineach subtree. There are two major advantages with the LT: One is dramaticallyreducing the running time of assigning noncenter data points to their clusterID, because the assigning process is turned into just disconnecting the linksfrom each center to its parent. The other is that the tree model forrepresenting clusters is more informative. Because we can check which objectsare more likely to be selected as centers in finer grained clustering, or whichobjects reach to its center via less jumps. Experiment results and analysisshow the effectiveness and efficiency of the assigning process with an LT.

Leading Tree in DPCLUS and Its Impact on Building Hierarchies