  DBSCAN has been widely used in density-based clustering algorithms. However,
with the increasing demand for Multi-density clustering, previous traditional
DSBCAN can not have good clustering results on Multi-density datasets. In order
to address this problem, an adaptive Multi-density DBSCAN algorithm
(AMD-DBSCAN) is proposed in this paper. An improved parameter adaptation method
is proposed in AMD-DBSCAN to search for multiple parameter pairs (i.e., Eps and
MinPts), which are the key parameters to determine the clustering results and
performance, therefore allowing the model to be applied to Multi-density
datasets. Moreover, only one hyperparameter is required for AMD-DBSCAN to avoid
the complicated repetitive initialization operations. Furthermore, the variance
of the number of neighbors (VNN) is proposed to measure the difference in
density between each cluster. The experimental results show that our AMD-DBSCAN
reduces execution time by an average of 75% due to lower algorithm complexity
compared with the traditional adaptive algorithm. In addition, AMD-DBSCAN
improves accuracy by 24.7% on average over the state-of-the-art design on
Multi-density datasets of extremely variable density, while having no
performance loss in Single-density scenarios. Our code and datasets are
available at https://github.com/AlexandreWANG915/AMD-DBSCAN.

Databases;Distributed, Parallel, and Cluster Computing;Machine Learning