Optimal Bandwidth Selection for DENCLUE Algorithm
This work addresses a specific, incremental improvement for algorithm engineers using density-based clustering on nonlinear data.
The paper tackles the parameter selection problem for the DENCLUE clustering algorithm, proposing a new approach to compute optimal parameters and reporting its performance in experiments.
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.