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# '''Sensitivity to Noise and Outliers''': Hierarchical clustering methods can be sensitive to noise and outliers in the data, which can lead to the formation of inaccurate or misleading cluster hierarchies <ref name="SLINK" />.
# '''Difficulty with High-Dimensional Data''': In high-dimensional spaces, hierarchical clustering can face challenges due to the curse of dimensionality, where data points become sparse, and distance measures become less meaningful. This can result in poorly defined clusters <ref name=":3" />.
# '''Inability to Handle Non-Convex Shapes and Varying Densities''': Traditional hierarchical clustering methods, like many other clustering algorithms, often assume that clusters are convex and have similar densities. They may struggle to accurately identify clusters with non-convex shapes or varying densities <ref>{{Cite journal |last=Wani |first=Aasim Ayaz |date=2024-08-29 |title=Comprehensive analysis of clustering algorithms: exploring limitations and innovative solutions
== Software ==
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