Zeng, Shijie and Wang, Yuefei and Wen, Yukun and Yu, Xi and Li, Binxiong and Wang, Zixu (2024) Firefly forest: A swarm iteration-free swarm intelligence clustering algorithm. Journal of King Saud University - Computer and Information Sciences, 36 (9). p. 102219. ISSN 1319-1578, DOI https://doi.org/10.1016/j.jksuci.2024.102219.
Full text not available from this repository.Abstract
The Firefly Forest algorithm is a novel bio-inspired clustering method designed to address key challenges in traditional clustering techniques, such as the need to set a fixed number of neighbors, predefine cluster numbers, and rely on computationally intensive swarm iterative processes. The algorithm begins by using an adaptive neighbor estimation, refined to filter outliers, to determine the brightness of each firefly. This brightness guides the formation of firefly trees, which are then merged into cohesive firefly forests, completing the clustering process. This approach allows the algorithm to dynamically capture both local and global patterns, eliminate the need for predefined cluster numbers, and operate with low computational complexity. Experiments involving 14 established clustering algorithms across 19 diverse datasets, using 8 evaluative metrics, demonstrate the Firefly Forest algorithm's superior accuracy and robustness. These results highlight its potential as a powerful tool for real-world clustering applications. Our code is available at: https://github.com/firesaku/FireflyForest.
| Item Type: | Article |
|---|---|
| Funders: | Sichuan Comic and Animation Research Center, Key Research Institute of Social Sciences of Sichuan Province (DM2024013), National Supercomputing Center in Chengdu-Chengdu University Branch, Sichuan, China |
| Uncontrolled Keywords: | Swarm Intelligence; Bio-inspired Clustering; Iteration Free; Outlier Filter |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 17 Feb 2025 04:16 |
| Last Modified: | 17 Feb 2025 04:16 |
| URI: | http://eprints.um.edu.my/id/eprint/47413 |
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