Leading Tree in DPCLUS and Its Impact on Building Hierarchies

06/12/2015
by   Ji Xu, et al.
0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

05/28/2018

Hierarchical clustering with deep Q-learning

The reconstruction and analyzation of high energy particle physics data ...
03/24/2020

Tree Index: A New Cluster Evaluation Technique

We introduce a cluster evaluation technique called Tree Index. Our Tree ...
01/16/2013

Model-Based Hierarchical Clustering

We present an approach to model-based hierarchical clustering by formula...
03/02/2022

A density peaks clustering algorithm with sparse search and K-d tree

Density peaks clustering has become a nova of clustering algorithm becau...
10/19/2016

Clustering by connection center evolution

The determination of cluster centers generally depends on the scale that...
12/24/2014

An Effective Semi-supervised Divisive Clustering Algorithm

Nowadays, data are generated massively and rapidly from scientific field...
12/20/2017

Fast kNN mode seeking clustering applied to active learning

A significantly faster algorithm is presented for the original kNN mode ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.