aspg-21-3631-1791465329 20261008131529 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Neutrosophic Science IJNS 2692-6148 2690-6805 2025 26 1 Sitikantha Mallik School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India Suneeta Mohanty School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India Bhabani Shankar Prasad Mishra School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India The most important stage of data mining is clustering. Several distinct clustering approaches like grid-based, density-based, partitioning, graph-based, model-based, and hierarchical clustering are used for cluster analysis. We can cluster data objects into hierarchical trees by using the hierarchical clustering approach. Hierarchical clustering, with its agglomerative and divisive types, uses nodes to represent clusters. Agglomerative clustering is favored, and high-quality clusters are essential for successful cluster analysis. Up to this point, numerous alternatives to the clustering technique have been proposed, including the fuzzy k-mean approach. The uncertainty resulting from numerical variations or unpredictable natural occurrences may be handled by any data mining techniques now in use. However, indeterminacy components may be present in current data mining challenges in real-world scenarios. Neutrosophic logic, applicable in various sectors, is gaining traction due to its efficiency and accuracy, attracting investment for its potential to improve human lives. The suggested approach outperforms current methods like fuzzy logic and k-means in its ability to forecast the number of clusters. 2025 243 253 3631 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJNS.260121 https://www.americaspg.com/journal/21/article/3631 https://www.americaspg.com/storage/61740198929.pdf