Related papers: Clustering Based on Graph of Density Topology

Clustering Based on Graph of Density Topology

URL: http://arxiv.org/abs/2009.11612v1
Date: Thu, 24 Sep 2020 11:40:24 GMT
Title: Clustering Based on Graph of Density Topology
Authors: Zhangyang Gao, Haitao Lin, Stan. Z Li
Abstract summary: We propose a novel clustering algorithm based on what we call graph of density topology (GDT) GDT achieves the SOTA performance by far on almost all the popular datasets, and has a low time complexity of O(nlogn)
Score: 41.57592288113871
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Data clustering with uneven distribution in high level noise is challenging. Currently, HDBSCAN is considered as the SOTA algorithm for this problem. In this paper, we propose a novel clustering algorithm based on what we call graph of density topology (GDT). GDT jointly considers the local and global structures of data samples: firstly forming local clusters based on a density growing process with a strategy for properly noise handling as well as cluster boundary detection; and then estimating a GDT from relationship between local clusters in terms of a connectivity measure, givingglobal topological graph. The connectivity, measuring similarity between neighboring local clusters, is based on local clusters rather than individual points, ensuring its robustness to even very large noise. Evaluation results on both toy and real-world datasets show that GDT achieves the SOTA performance by far on almost all the popular datasets, and has a low time complexity of O(nlogn). The code is available at https://github.com/gaozhangyang/DGC.git.

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