Data Mining Algorithms Including DBSCAN and K-Means

In our research, we employed various data mining algorithms, including DBSCAN and K-Means, which served as fundamental tools for successful data analysis and interpretation. The DBSCAN algorithm operates through density-connected clustering, where it identifies core points with sufficient neighboring points within a specified radius (eps) and expands clusters by connecting density-reachable points. This makes it particularly effective for discovering arbitrary-shaped clusters and handling noise points. The K-Means algorithm utilizes centroid-based partitioning, where it iteratively assigns data points to the nearest cluster center and updates centroids by calculating the mean of assigned points until convergence. Notably, both algorithms demonstrate excellent performance when processing large-scale datasets due to their efficient handling of high-dimensional data and rapid search capabilities within data space. We believe these algorithms will play increasingly important roles in future data analytics applications, especially when implemented with optimization techniques like KD-tree for neighborhood searches in DBSCAN or K-Means++ initialization for improved centroid selection.