LS-BMO-HDBSCAN as a hybrid memetic bacterial intelligence framework for efficient data clustering.

ABSTRACT: Data mining provides insights from massive and complex data to help make informed decisions, find hidden patterns, and solve difficult real-world problems. Unsupervised clustering groups natural data without labeling. Classic clustering algorithms like K-Means are popular due to their simplicity and computational efficiency, but they are sensitive to initialization and cannot handle noisy or non-convex cluster topologies. A hybrid clustering technique integrating L-SHADE, Bacterial Memetic Optimization (BMO), and K-means initialized HDBSCAN overcomes these concerns. The proposed system uses L-SHADE's adaptive parameter management and convergence efficiency, BMO's exploration-exploitation balance and memetic learning, HDBSCAN's density-aware, noise-resilient clustering, and K-Means for cent

SUBMITTER: Al-Nussairi AKJ 

PROVIDER: S-EPMC12630809 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature