  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.
