Clustering by Deep Nearest Neighbor Descent (D-NND): A Density-based Parameter-Insensitive Clustering Method

by   Teng Qiu, et al.

Most density-based clustering methods largely rely on how well the underlying density is estimated. However, density estimation itself is also a challenging problem, especially the determination of the kernel bandwidth. A large bandwidth could lead to the over-smoothed density estimation in which the number of density peaks could be less than the true clusters, while a small bandwidth could lead to the under-smoothed density estimation in which spurious density peaks, or called the "ripple noise", would be generated in the estimated density. In this paper, we propose a density-based hierarchical clustering method, called the Deep Nearest Neighbor Descent (D-NND), which could learn the underlying density structure layer by layer and capture the cluster structure at the same time. The over-smoothed density estimation could be largely avoided and the negative effect of the under-estimated cases could be also largely reduced. Overall, D-NND presents not only the strong capability of discovering the underlying cluster structure but also the remarkable reliability due to its insensitivity to parameters.



There are no comments yet.


page 9

page 11

page 16

page 23

page 24


Eigenvalue Analysis via Kernel Density Estimation

In this paper, we propose an eigenvalue analysis -- of system dynamics m...

Analysis of KNN Density Estimation

We analyze the ℓ_1 and ℓ_∞ convergence rates of k nearest neighbor densi...

Clustering Large Data Sets with Incremental Estimation of Low-density Separating Hyperplanes

An efficient method for obtaining low-density hyperplane separators in t...

Numerical performance of Penalized Comparison to Overfitting for multivariate kernel density estimation

Kernel density estimation is a well known method involving a smoothing p...

Clustering by Hierarchical Nearest Neighbor Descent (H-NND)

Previously in 2014, we proposed the Nearest Descent (ND) method, capable...

Universal Approximation of Edge Density in Large Graphs

In this paper, we present a novel way to summarize the structure of larg...

Sea Clutter Distribution Modeling: A Kernel Density Estimation Approach

An accurate sea clutter distribution is crucial for decision region dete...
This week in AI

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