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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

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

Ahmed Kateb Jumaah Al-Nussairi¹, Abdulsalam Abdulsattar Abdulazez², Ahmed Adnan Hadi³

¹Al-Manara College for Medical Sciences, Amarah, Iraq.

Scientific Reports

|November 19, 2025

Summary

A new hybrid clustering method, LS-BMO-HDBSCAN, enhances data mining by combining L-SHADE, Bacterial Memetic Optimization (BMO), and K-means initialized HDBSCAN for superior performance in complex datasets.

Keywords:

Data clustering Dynamic mutation Optimization technique

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Area of Science:

Data Mining
Machine Learning
Computational Intelligence

Background:

Unsupervised clustering is vital for pattern discovery in large datasets.
Traditional algorithms like K-Means struggle with noisy data and complex cluster shapes.
Existing methods often lack robustness and adaptability to diverse data structures.

Purpose of the Study:

To develop an advanced hybrid clustering technique for improved data mining.
To address limitations of classic clustering algorithms in handling noisy and high-dimensional data.
To enhance global and local search capabilities and prevent premature convergence.

Main Methods:

Integration of L-SHADE for adaptive parameter control and BMO for exploration-exploitation balance.
Utilizing K-means for centroid initialization within HDBSCAN for density-aware clustering.
Proposed LS-BMO-HDBSCAN method evaluated on eleven benchmark datasets.

Main Results:

LS-BMO-HDBSCAN demonstrated superior performance across all tested datasets.
The hybrid approach outperformed established algorithms like K-Means and PSO variants.
Key metrics including Silhouette Score, Davies-Bouldin Index, Rand Index, and Jaccard Index showed significant improvements.

Conclusions:

The LS-BMO-HDBSCAN technique offers enhanced durability, adaptability, and accuracy in clustering.
This novel method reliably solves complex clustering problems in real-world data mining.
The hybrid approach represents a significant advancement in intelligent data analysis.