Fully Automated Density-Based Clustering Method

Cluster analysis is a crucial technique in unsupervised machine learning, pattern recognition, and data analysis. However, current clustering algorithms suffer from the need for manual determination of parameter values, low accuracy, and inconsistent performance concerning data size and structure. To address these challenges, a novel clustering algorithm called the fully automated density-based clustering method (FADBC) is proposed. The FADBC method consists of two stages: parameter selection and cluster extraction. In the first stage, a proposed method extracts optimal parameters for the dataset, including the epsilon size and a minimum number of points thresholds. These parameters are then used in a density-based technique to scan each point in the dataset and evaluate neighborhood densities to find clusters. The proposed method was evaluated on different benchmark datasets and metrics, and the experimental results demonstrate its competitive performance without requiring manual inputs. The results show that the FADBC method outperforms well-known clustering methods such as the agglomerative hierarchical method, k-means, spectral clustering, DBSCAN, FCDCSD, Gaussian mixtures, and density-based spatial clustering methods. It can handle any kind of data set well and perform excellently.