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DBSCAN has been widely used in density-based clustering algorithms. However,with the increasing demand for Multi-density clustering, previous traditionalDSBCAN can not have good clustering results on Multi-density datasets. In orderto address this problem, an adaptive Multi-density DBSCAN algorithm(AMD-DBSCAN) is proposed in this paper. An improved parameter adaptation methodis proposed in AMD-DBSCAN to search for multiple parameter pairs (i.e., Eps andMinPts), which are the key parameters to determine the clustering results andperformance, therefore allowing the model to be applied to Multi-densitydatasets. Moreover, only one hyperparameter is required for AMD-DBSCAN to avoidthe complicated repetitive initialization operations. Furthermore, the varianceof the number of neighbors (VNN) is proposed to measure the difference indensity between each cluster. The experimental results show that our AMD-DBSCANreduces execution time by an average of 75% due to lower algorithm complexitycompared with the traditional adaptive algorithm. In addition, AMD-DBSCANimproves accuracy by 24.7% on average over the state-of-the-art design onMulti-density datasets of extremely variable density, while having noperformance loss in Single-density scenarios. Our code and datasets areavailable at https://github.com/AlexandreWANG915/AMD-DBSCAN.

AMD-DBSCAN: An Adaptive Multi-density DBSCAN for datasets of extremely variable density