Nonparametric Nearest Neighbor Descent Clustering based on Delaunay Triangulation

2015/02/17 by Teng Qiu, Yongjie Li, Qiu, Teng +1
Computer Science · #Advanced Clustering Algorithms Research #Computer Vision and Pattern Recognition (cs.CV) #Data Management and Algorithms #Data Visualization and Analytics #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1502.04837

openalex publication_date 2015/02/17 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

In our physically inspired in-tree (IT) based clustering algorithm and the series after it, there is only one free parameter involved in computing the potential value of each point. In this work, based on the Delaunay Triangulation or its dual Voronoi tessellation, we propose a nonparametric process to compute potential values by the local information. This computation, though nonparametric, is relatively very rough, and consequently, many local extreme points will be generated. However, unlike those gradient-based methods, our IT-based methods are generally insensitive to those local extremes. This positively demonstrates the superiority of these parametric (previous) and nonparametric (in this work) IT-based methods.

Citations

• An Effective Semi-supervised Divisive Clustering Algorithm
• Cluster identification via Voronoi tessellation

Related

• Nearest Descent, In-Tree, and Clustering
• Adaptive Nonparametric Clustering
• Clustering by Descending to the Nearest Neighbor in the Delaunay Graph Space
• DelTriC: A Novel Clustering Method with Accurate Outlier
• A general framework for the IT-based clustering methods