Volume 26 , Issue 1 , PP: 243-253, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Sitikantha Mallik 1 * , Suneeta Mohanty 2 , Bhabani Shankar Prasad Mishra 3
Doi: https://doi.org/10.54216/IJNS.260121
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.
Indeterminacy , Clustering , Hierarchical clustering algorithm , Uncertainty , Silhouette coefficient
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