Density-based algorithms, like DBSCAN, group data points based on density. Gaussian Mixture Models capture data distribution using probability models. Spectral clustering leverages graph theory to ...

Density-based clustering can handle arbitrary shapes and sizes of clusters, as well as noise and outliers, but it may not work well with varying densities or high-dimensional data.

Density-based spatial clustering of applications with noise (DBSCAN) is a well-known data clustering algorithm that is commonly used in data mining and machine learning. Based on a set of points ...

Moulavi, Davoud, et al. "Density-based clustering validation." Proceedings of the 2014 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2014. How do you ...

Density-based clustering algorithms have recently gained popularity in the data mining field due to their ability to discover arbitrary shaped clusters while preserving spatial proximity of data ...

F. Cao, M. Ester, W. Qian and A. Zhou, “Density-Based Clustering over an Evolving Data Stream with Noise,” SIAM Conference on Data Mining, 2006, pp. 328-339. has been cited by the following article: ...

A. Amini and W. Teh Ying, “Density Micro-Clustering Algorithms on Data Streams A Review,” International Conference on Data Mining and Applications (ICDMA), Hong Kong, 2011, pp. 410-414.