Volume 26 • Issue 1 • PP: 243-253 • 2025
Neutrosophic Hierarchical Clustering: A Novel Approach for Handling Uncertainty in Multi-Level Data Organization
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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.
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References
[1] R. T. Ng and J. Han, "Efficient and effective clustering methods for spatial data mining," in *Proc. 20th Int. Conf. Very Large Data Bases (VLDB '94), San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1994, pp. 144–155.
[2] F. Smarandache, Ed., *Proceedings of the First International Conference on Neutrosophy, Neutrosophic Logic, Neutrosophic Set, Neutrosophic Probability and Statistics, Univ. of New Mexico, Gallup Campus, Xiquan, Phoenix, 2002, p. 147.
[3] R. R. Yager and A. T. de Almeida, "On the use of neutrosophic sets in decision making," Information Sciences, vol. 572, pp. 358–371, 2021. [Online]. Available: https://doi.org/10.1016/j.ins.2021.06.052
[4] M. N. Murty, A. K. Jain, and P. J. Flynn, "Data clustering: A review," ACM Comput. Surv, vol. 31, no. 3, pp. 264–323, 1999.
[5] W. B. Xie, Z. Liu, D. Das, B. Chen, and J. Srivastava, "Scalable clustering by aggregating representatives in hierarchical groups," Pattern Recognit., vol. 136, p. 109230, 2023.
[6] H. Hu, J. Liu, X. Zhang, and M. Fang, "An effective and adaptable k-means algorithm for big data cluster analysis," Pattern Recognit., vol. 139, p. 109404, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S003132032300105X
[7] F. M. G. C. A. Cimino, B. Lazzerini, and F. Marcelloni, "A novel approach to fuzzy clustering based on a dissimilarity relation extracted from data using a TS system," Pattern Recognit., vol. 39, no. 11, pp. 2077–2091, 2006.
[8] A. Q. Ansari, R. Biswas, and S. Aggarwal, "Neutrosophic classifier: An extension of fuzzy classifier," Appl. Soft Comput., vol. 13, no. 1, pp. 563–573, Jan. 2013. [Online]. Available: https://doi.org/10.1016/j.asoc.2012.08.002
[9] U. Rivieccio, "Neutrosophic logics: Prospects and problems," Fuzzy Sets Syst., vol. 159, no. 14, 2008. [Online]. Available: https://doi.org/10.1016/j.fss.2007.11.011
[10] F. Smarandache, A Unifying Field in Logics: Neutrosophic Logic. In: Neutrosophy, Neutrosophic Set, Neutrosophic Probability: Neutrosophic Logic, Santa Fe, NM, USA: American Research Press, 2005.
[11] M. G. Gafar, M. Elhoseny, and M. Gunasekaran, "Modeling neutrosophic variables based on particle swarm optimization and information theory measures for forest fires," J. Supercomput., vol. 76, pp. 2339–2356, 2020. [Online]. Available: https://doi.org/10.1007/s11227-018-2512-5
[12] M. K. Pakhira, S. Bandyopadhyay, and U. Maulik, "Validity index for crisp and fuzzy clusters," Pattern Recognit., vol. 37, no. 3, pp. 487–501, 2004. [Online]. Available: https://doi.org/10.1016/j.patcog.2003.06.005
[13] A. Gallo-Bueno, M. Reynaud, M. Casas-Cabanas, and J. Carrasco, "Unsupervised machine learning to classify crystal structures according to their structural distortion: A case study on Li-argyrodite solid-state electrolytes," Energy AI, vol. 9, p. 100159, 2022. [Online]. Available: https://doi.org/10.1016/j.egyai.2022.100159
[14] B. Desgraupes, "Clustering indices," Univ. Paris Ouest-Lab Modal’X, vol. 1, no. 34, 2013.
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