Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/1675 2018 2018 Department of Mathematics, Education College, University of AL-Hamdaniya Sarah G. M. Al- Kababchee Department of Statistics and Informatics, University of Mosul, 41002 Mosul, Iraq; College of Engineering, University of Warith Al-Anbiyaa, 56001 Karbala, Iraq Zakariya Y. Algamal Department of Mathematics, University of Mosul, Mosul, Iraq Omar S. Qasim This paper presents an improved penalized regression-based clustering algorithm using a nature-inspired approach. Clustering is an unsupervised learning method widely used in data fusion mining, including gene analysis, to group unclassified fusion data based on their features. The proposed algorithm is an extension of the "Sum of Norms" model and aims to better estimate the data by fusing information from various sources. The performance of the proposed algorithm is evaluated on gene expression data. Results show that our approach outperforms other methods, indicating its potential impact on clustering research with data fusion. 2023 2023 70 76 10.54216/FPA.110105 https://www.americaspg.com/articleinfo/3/show/1675