Optimal Bandwidth Selection for DENCLUE Algorithm

TL;DR

This paper introduces a new method for selecting optimal bandwidth parameters for the DENCLUE clustering algorithm, addressing a previously neglected issue and improving its performance on nonlinear data structures.

Contribution

It proposes a novel approach to compute optimal parameters for DENCLUE, enhancing its effectiveness in clustering nonlinear data.

Findings

The new method improves clustering accuracy on nonlinear datasets

Optimal bandwidth selection enhances DENCLUE's performance

Experimental results validate the effectiveness of the proposed approach

Abstract

In modern day industry, clustering algorithms are daily routines of algorithm engineers. Although clustering algorithms experienced rapid growth before 2010. Innovation related to the research topic has stagnated after deep learning became the de facto industrial standard for machine learning applications. In 2007, a density-based clustering algorithm named DENCLUE was invented to solve clustering problem for nonlinear data structures. However, its parameter selection problem was largely neglected until 2011. In this paper, we propose a new approach to compute the optimal parameters for the DENCLUE algorithm, and discuss its performance in the experiment section.