The Challenges of Clustering High Dimensional Data

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Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators (2011, unknown): “as the dimensionality increases [79].”
K-means properties on six clustering benchmark datasets (2018, methodology): “[9] used histograms of the distances to estimate whether the data have clusters.”
Unsupervised Deep Embedding for Clustering Analysis (2015, background): “However, their distance metrics are limited to the original data space and they tend to be ineffective when input dimensionality is high (Steinbach et al., 2004).”
An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data (2007, background): “Clearly, clustering of high-dimensional sparse data requires special treatment [2], [3], [4], [5].”
A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013, background): “In particular, in high-dimensional data, traditional similarity measures, as used in conventional clustering algorithms, are usually not meaningful (Kriegel et al. 2009; Steinbach et al. 2003).”
BERTopic: Neural topic modeling with a class-based TF-IDF procedure (2022, unknown): “Although clustering approaches exist for over-coming this curse of dimensionality (Pandove et al., 2018; Steinbach et al., 2004), a more straight-forward approach is to reduce the dimensionality of embeddings.”