2015/06/12 by Ji Xu, Xu Ji, Guoyin Wang +2
Computer Science · #Advanced Clustering Algorithms Research #Artificial Intelligence (cs.AI) #Data Management and Algorithms #Data Mining Algorithms and Applications #FOS: Computer and information sciences #cs.AI
paper · pdf · doi:10.48550/arxiv.1506.03879
11 Pages, 5 figures. It is a very fundamental topic with respect to the research (clustering by fast search and find of density peaks)
openalex publication_date 2015/06/12 · arxiv created 2015/06/15 · arxiv updated 2015/06/16 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.