TL;DR
An Enhanced Density Peak Clustering (EDPC) method is proposed, which creatively incorporates multilayer perceptron (MLP)-based dimensionality reduction and a hierarchical label assignment strategy to improve clustering performance in high-dimensional scenarios.
This work proposed to use Regularised Multidimensional Scaling using Radial Basis Function (RBF-MDS) for dimension reduction, a multidimensional scaling that mitigates the impact of irrelevant or redundant features, enabling 2D/3D visualisation of clusters for interpretability.
Density peak clustering (DPC) connects each observation to its nearest neighbor of higher density and identifies cluster centers as high-density observations with unusually large nearest neighbor uphill shifts. The resulting uphill paths from observations to cluster centers, however, can be irregular and unstable in lo...
An outlier detection algorithm based on affinity density peak clustering (ADPOD) is proposed and it is demonstrated that the proposed algorithm significantly outperforms state-of-the-art competitors.
Clustering is an unsupervised machine learning technique used to identify and group similar data points within large datasets without relying on predefined class labels or target outcomes. It plays an important role in data mining by facilitating the discovery of meaningful groups, hidden patterns, and relevant data di...
Density peaks clustering (DPC) is a representative density-based clustering method. However, conventional DPC usually relies on manual cluster-center selection or prior knowledge of the cluster number, and may suffer from unstable label assignment on complex data distributions. To improve the automation capability and...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
