Library · Machine learning & AI
What Hierarchical Clustering got wrong, from 44 theses
Summary written by a model from the records below: The records document multiple scenarios where hierarchical clustering struggled with steep computational scaling, sensitivity to noise, and highly unbalanced cluster distributions. Researchers frequently abandoned or rejected hierarchical methods when simpler partitional algorithms or alternative linkage criteria provided better cluster separation and practical stability. These records come from graduate theses, 2021 to 2026, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 8 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Hierarchical clustering degrades under noise, uninformative feature variation, and outliers
Hierarchical clustering frequently broke down when applied to datasets contaminated by noise residuals, high transcriptional heterogeneity, or mixed feature variables. In these scenarios, the algorithms misassigned outlier values, picked up dominant non-pathological variations, and failed to uncover meaningful cluster structures.
Tried and failed
hierarchical clustering of pseudotime-dependent gene expression applied to single-cell transcriptome trajectories. Outcome: no signal. Reason: high transcriptional heterogeneity prevented meaningful ordering by genotype or time point
Tried and failed
hierarchical clustering of cross-condition effect correlations applied to functional pathway discovery from QTLs. Outcome: no signal. Reason: most clusters lacked functional pathway enrichment across diverse environments
Understanding the Forces that Shape Evolution · Harvard · PhD
Considered and rejected
Considered and rejected: Rejected single-linkage and complete-linkage hierarchical clustering due to extreme sensitivity to noise/outliers when devices share few overlapping points.
Characterizing and Detecting Physical Layer Issues in Cable Broadband Networks · Duke · PhD
Considered and rejected
Considered and rejected: Rejected using latitude and longitude features in hierarchical clustering for rainfall up-scaling because spatial coordinates dominated and degraded precipitation pattern grouping.
Underpinning research on the dynamical aspects of one-dimensional nonlinear lattices · University College Dublin · PhD
Considered and rejected
Considered and rejected: Rejected K-means and hierarchical clustering for final spatio-temporal hotspot analysis because they cannot remove noise points and require pre-specifying cluster counts.
IDENTIFYING PROBABLE MARITIME PIRACY EVENTS USING MARITIME INCIDENT DATA · Naval Postgraduate School · Master's
Tried and failed
density-based and hierarchical clustering algorithms applied to low-energy sensor impact classification. Outcome: unstable. Reason: overly sensitive to hyperparameter selection and outlier noise in weak signals
Machine learning techniques for impact characterization and vibration based damage detection for composite structures · Imperial · PhD
Tried and failed
hierarchical clustering on raw noise residuals applied to synthetic image detection. Outcome: no signal. Reason: additive random noise dominated residual space causing impure mixed clusters
Defending Against Misuse of Synthetic Media: Characterizing Real-world Challenges and Building Robust Defenses · Virginia Tech · Doctorate
Tried and failed
Hierarchical clustering on unsmoothed time series applied to Short lifecycle demand curves. Outcome: no signal. Reason: Unsmoothed noise prevented the dendrogram from separating curves according to underlying structural patterns.
Optimizing Sales Forecasting, Inventory, Pricing and Sourcing Decisions · EPFL · PhD
Tried and failed
hierarchical clustering with combined feature sets applied to heterogeneous behavioral time-series data. Outcome: no signal. Reason: Simultaneous clustering of mixed intensity and temporal variables obscured meaningful segment structure.
Tried and failed
hierarchical clustering and t-SNE dimensionality reduction applied to high-dimensional perturbation log-fold change data. Outcome: no signal. Reason: Unsupervised clustering failed to separate distinct biological groups based on variable feature fold changes
Studying the tissue-specificity of cancer driver genes through KRAS and genetic dependency screens. · Harvard · PhD
Tried and failed
agglomerative hierarchical clustering with Ward's linkage applied to quantitative MRI tumor habitat segmentation. Outcome: worse than baseline. Reason: misassigned outlier high-value voxels to low-value clusters rather than isolating distinct habitats
Toward advances in data acquisition and analysis for quantitative multi-contrast magnetic resonance imaging · UT Austin · PhD
Hierarchical clustering underperforms alternative partitioning and tree baselines
Hierarchical clustering was repeatedly outperformed by alternatives such as k-means, Leiden clustering, and level-set trees on metrics like cluster separation and recovery accuracy. Researchers also rejected hierarchical clustering because early agglomerative or divisive decisions cannot be iteratively reassigned like discrete partitioning methods.
Lost to a baseline
Hierarchical clustering of PCA-projected phenotype profile correlations achieved higher precision at lower recall (<0.2) compared to Leiden clustering on the PHATE diffusion affinity graph.
Image-based pooled genetic screens for complex cellular phenotypes · MIT · PhD
Lost to a baseline
Hierarchical clustering of phenotype profile correlations demonstrated higher precision at lower recall (<0.2) than Leiden clustering on the PHATE diffusion affinity graph.
Image-based pooled genetic screens for complex cellular phenotypes · MIT · PhD
Lost to a baseline
Ward's hierarchical clustering on non-linear CUT GAN manifold produced less explainable and poorly separated phenotypic clusters compared to the simpler log-linear regression residual baseline
Brain Metabolic Responses To Alzheimer Pathologies With Molecular Imaging And Machine Learning · Penn · PhD
Lost to a baseline
K-means, K-medoid, DBSCAN, and hierarchical clustering (single and Ward linkage) were beaten by level-set tree clustering (~95% cumulative accuracy on baseline gene OCR cluster recovery).
Categorizing the dispersion of open chromatin usage patterns of Treg related genes using an variational autoencoder based algorithm · Harvard · Master's
Lost to a baseline
Visual stimulus-based hierarchical clustering performed worse than electrical STA-based clustering at separating distinct ON visual types in rd10 degenerated retina (blending all ON cells into OFF clusters).
Classifying Retinal Ganglion Cells for Bionic Vision · Tübingen · PhD
Lost to a baseline
Hierarchical clustering with Ward's method achieved a suboptimal criterion sum of BDs (7.09 for k=2, 4.67 for k=3) compared to k-means algorithms (6.46 and 4.48/4.56).
New methods in home-range overlap and clustering · Iowa State · PhD
Lost to a baseline
On the 4-Gaussian mixture dataset in R^2 (n=6,000), Average Linkage hierarchical clustering (0.664 NMI) lost to k-Means (0.797 NMI), Spectral Clustering (0.805 NMI), Divisive Spectral (0.820 NMI), and Tangles (0.829 NMI).
Graphs in Unsupervised Machine Learning · Tübingen · PhD
Considered and rejected
Considered and rejected: Rejected discrete mode clustering via DBSCAN, k-medoids, spectral clustering, and hierarchical clustering in favor of k-means for simplicity and explicit cluster count control.
Modeling and Control of Networked Systems: Applications to Air Transportation · MIT · PhD
Considered and rejected
Considered and rejected: Rejected hierarchical clustering because divisive/agglomerative splits/merges cannot be reassigned iteratively, opting instead for discrete partitioning via k-medoids (PAM).
Exposure and Attitudes: Informing Upstream Barriers to Safe Systems via Machine Learning, Bayesian Estimation, and Attitudinal Research · Georgia Tech · PhD
High computational complexity and memory scaling prevent deployment on large datasets
Agglomerative and divisive hierarchical algorithms suffered from steep quadratic, cubic, or exponential time complexities and excessive pairwise distance calculations. These resource demands caused memory exhaustion, repeated system crashes, and severe bottlenecks on large graphs and high-dimensional datasets.
Considered and rejected
Considered and rejected: Rejected agglomerative hierarchical clustering with dendrograms because it crashed repeatedly due to dataset size
A MACHINE LEARNING APPROACH TO NETWORK SECURITY CLASSIFICATION USING NETFLOW DATA · Naval Postgraduate School · Master's
Considered and rejected
Considered and rejected: Rejected divisive hierarchical clustering due to its algorithmic time complexity of O(2^N) compared to O(N^2 log N) - O(N^3) for agglomerative clustering
A machine learning approach to develop turbulence closures using clustering and neural networks · Imperial · PhD
Tried and failed
hierarchical agglomeration modularity maximization applied to large-scale bipartite projection graphs. Outcome: too slow. Reason: computational time and memory scaled poorly on very large dense projected constraint graphs
Data-Driven Decision Analytics in Complex Systems: Information Infrastructure, Problem Decomposition, and Algorithmic Design · Georgia Tech · PhD
Lost to a baseline
Hierarchical clustering with embedded cluster evaluation matches T-SNE/DBSCAN speed on small systems (5-bus) but scales as O(n^3) while T-SNE/DBSCAN remains linear/polynomial
Operational moving target defences for improved power system cyber-physical security · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected divisive hierarchical clustering strategies due to computational expense (2^{N-1} - 1 possible partitions).
Robust Bayesian Anomaly Detection Methods for Large Scale Sensor Systems · Virginia Tech · Doctorate
Considered and rejected
Considered and rejected: Rejected hierarchical clustering as the backbone engine for TDC because it cannot undo suboptimal early merges and has high computational complexity
Data-Driven Mobile Traffic Modelling in Cellular Networks · Politecnico di Torino · PhD
Considered and rejected
Considered and rejected: Standard Euclidean hierarchical clustering for mass spectrometry imaging pixel data due to high computational overhead (billions of pairwise distance calculations).
Comparative analysis of sporadic and colitis associated cancers · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected AI/generative models (e.g., Ward hierarchical clustering, generative networks) for real-time edge/cloud prediction due to high O(n^2)-O(n^3) computational overhead.
Contribuciones al ahorro de energía en redes de sensores inalámbricos aplicados a la piscicultura · University of Las Palmas de Gran Canaria · PhD
Considered and rejected
Considered and rejected: Rejected divisive hierarchical clustering and density-based clustering (DBSCAN) due to computational expense and sensitivity to MinPts/Eps parameters.
Hierarchical clustering produces highly unbalanced partitions and unstable groupings
Hierarchical approaches frequently produced skewed distributions where one or two dominant clusters swallowed most points while minority classes were dropped. Attempting deeper cluster divisions or threshold adjustments led to single-cluster collapse, tiny uninterpretable clusters, and unstable results across real-world datasets.
Tried and failed
k-means and hierarchical clustering applied to pairwise co-occurrence and embedding distances. Reason: Produced highly unbalanced partitions dominated by one or two massive clusters
Deconstructing multiple long-term conditions: methods and applications of clustering diseases and people · Imperial · PhD
Tried and failed
hierarchical agglomerative clustering applied to clinical feature subtyping. Reason: produced highly uneven cluster size distributions
A MULTI-MODAL APPROACH TO THE COMPLEXITIES OF ENDOMETRIOSIS: ENVIRONMENTAL, CLINICAL, AND MULTI-OMIC · Penn · PhD
Tried and failed
relaxed cluster radius termination thresholds applied to hierarchical bisecting k-means clustering. Outcome: worse than baseline. Reason: larger radius thresholds yielded no significant improvements in clustering quality or efficiency
Clustering and fast search of high dimensional big data · Texas Tech · PhD
Tried and failed
Hierarchical clustering with Ward's linkage applied to Vibration spectral feature embeddings. Reason: Failed to handle clusters with varying shapes and densities, dropping recall on minority classes
Vibration Event Detection and Classification in an Instrumented Building · Virginia Tech · Master's
Considered and rejected
Considered and rejected: Avoided standard k-means 1D splits in hierarchical bisection when cluster sizes/densities differ significantly, preferring KDE-based thresholding.
Algorithms for Data Fusion, Representation Learning, and Scalable Clustering based on Constrained Low-Rank Approximation · Georgia Tech · PhD
Tried and failed
McQuitty hierarchical clustering applied to multivariate spatial raster data. Outcome: worse than baseline. Reason: Collapsed into a single cluster or generated clusters without statistically significant differences.
The essential role of soil maps to support precision agriculture · Iowa State · Master's
Tried and failed
hierarchical clustering with high cluster numbers applied to voxel response profile patterns. Outcome: unstable. Reason: subsequent cluster divisions yielded unstable or tiny clusters that were uninterpretable
Action Representations in the Visual System · Harvard · PhD
Tried and failed
density-based hierarchical trajectory clustering applied to spatial trajectory datasets. Outcome: data insufficient. Reason: Low-volume minority trajectory patterns fell below the minimum cluster size threshold and were excluded as noise.
Hierarchical Behavior Models for Characterizing Trajectories within Terminal Airspace · MIT · PhD
Tried and failed
resampled parameter stacking with hierarchical clustering applied to crustal seismic receiver functions. Outcome: unstable. Reason: produced unreliable results across diverse real-world stations despite prior literature reports
Crustal seismic structure of the eastern Mediterranean: evidence from broadband seismology · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected stations/clusters containing <15 data points in hierarchical cluster analysis because they produce unrepresentatively small within-cluster variances.
Crustal seismic structure of the eastern Mediterranean: evidence from broadband seismology · Imperial · PhD
Specific linkage criteria fail to capture valid cluster structure or violate merge properties
Common criteria such as single, complete, and average linkage were rejected because they caused sparse divisions, ignored cluster structure, or yielded lower agglomerative coefficients than Ward linkage. In addition, centroid linkage violated monotonicity of merges, exponential linkage failed to surpass simple average linkage, and Ward linkage proved mathematically incompatible with certain non-Euclidean dissimilarity measures.
Tried and failed
exponential linkage hierarchical agglomerative clustering applied to entity normalization. Outcome: worse than baseline. Reason: failed to outperform standard average linkage despite interpolating between linkage criteria
A pipeline for data and knowledge extraction from material science literature to accelerate scientific discovery · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected single linkage and complete linkage hierarchical clustering because they fail to account for cluster structure.
Considered and rejected
Considered and rejected: Centroid linkage criterion for hierarchical clustering was rejected due to violation of monotonicity of merges.
A systematic design for applying personalised herbal medicine recommendations for type 2 diabetes · Imperial · PhD
Considered and rejected
Considered and rejected: Hierarchical clustering on the RCC genetic divergence matrix using Complete or Single linkage algorithms instead of Ward.D.
Statistical Methods For Multi-Omics Inference From Single Cell Transcriptome · Penn · PhD
Considered and rejected
Considered and rejected: Rejected using Ward's linkage method directly for hierarchical agglomerative cluster analysis because it is mathematically incompatible with Bray-Curtis dissimilarity.
Floristics, Conservation, and Restoration of Virginia's Piedmont Grasslands · Virginia Tech · Doctorate
Considered and rejected
Considered and rejected: Complete and average data linkage methods were evaluated for hierarchical clustering but were rejected because they resulted in more sparsely divided data.
The application of novel analytic methods to gain new insights in historically well-studied areas of perinatal epidemiology · Boston University · PhD
Considered and rejected
Considered and rejected: Rejected complete, single, and average hierarchical clustering methods in favor of Ward's method based on agglomerative coefficient comparisons.
A Pedagogical Approach to Create and Assess Domain-Specific Data Science Learning Materials in the Biomedical Sciences · Virginia Tech · Doctorate
Considered and rejected
Considered and rejected: Rejected complete linkage, single linkage, and UPGMA hierarchical clustering algorithms in favor of Ward's minimum variance method due to Ward's higher agglomerative coefficient.
Large sample statistical estimation of streamflow sensitivity to climate and land cover change · Oxford · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Develop hierarchical metrics to quantify clusterness and trajectoriness on subsets and individual clusters in single-cell datasets. Blocker: None
Developing Graph-based Computational Algorithms for Single-cell Data Science · Georgia Tech · PhD
Left open
Analyze why Ward hierarchical clustering outperforms Neighbor-Joining on specific metric-learned cell embedding spaces. Blocker: None
Reconstructing Cell Lineage Trees from Phenotypic Features with Metric Learning · Penn · PhD
Left open
Compare the satellite clustering performance of k-means against hierarchical clustering algorithms like CHAMELEON or HDBSCAN on the longitudinal tracking dataset. Blocker: None
Measuring Compliance to the International Telecommunication Union’s Geosynchronous Orbital Assignments · MIT · PhD
Left open
Evaluate hierarchical and density-based clustering algorithms against existing approaches for grouping user interests in news recommender systems. Blocker: None
Exploring Media Bias in News Recommender Systems: From Detection to Mitigation · University College Dublin · PhD
Left open
Compare urban land-use clustering solutions generated by DBSCAN, HDBSCAN, and hierarchical clustering algorithms on spatial data. Blocker: None
Physics-guided inference from gas emission data for source characterization and air pollution mapping · Cornell · PhD
Left open
Develop quantitative stopping criteria to assess representativeness and quality of training samples selected via hierarchical clustering. Blocker: None
A Structure-based Machine Learning Approach for Spatiotemporal Fluctuations in Nuclear Thermal Fluids · MIT · PhD
Left open
Develop improved methods for selecting representative data points within clusters to better estimate posterior distributions in hierarchical dataset selection. Blocker: The task is described purely as a vague direction without specific proposed alternative sampling methods or concrete targets.
Hierarchical Bayesian Dataset Selection · Virginia Tech · Master's
Left open
Develop dynamic clustering extensions for non-metric or high-dimensional graph representations beyond approximate hierarchical agglomerative clustering. Blocker: No concrete algorithmic formulation, data structures, or performance target specifications are provided.
Parallel Algorithms, Optimizations, and Benchmarks for Metric and Graph Clustering · MIT · PhD
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Luke Benjamin Funk · MIT · PhD · 2022
Image-based pooled genetic screens for complex cellular phenotypes - Christopher Steven Ogden · Imperial · PhD · 2022
Crustal seismic structure of the eastern Mediterranean: evidence from broadband seismology - A Halner · Oxford · PhD · 2022
Re-defining exacerbations of chronic obstructive pulmonary disease and developing tools to characterise heterogeneity and treatment response - Yujia Xie · Georgia Tech · PhD · 2026
Data-Driven Decision Analytics in Complex Systems: Information Infrastructure, Problem Decomposition, and Algorithmic Design - Lindsay Ann Guare · Penn · PhD · 2026
A MULTI-MODAL APPROACH TO THE COMPLEXITIES OF ENDOMETRIOSIS: ENVIRONMENTAL, CLINICAL, AND MULTI-OMIC - Dongjin Choi · Georgia Tech · PhD · 2025
Algorithms for Data Fusion, Representation Learning, and Scalable Clustering based on Constrained Low-Rank Approximation - Eliot Frederick Fenton · Harvard · PhD · 2025
Understanding the Forces that Shape Evolution - Solveig Peter · Tübingen · PhD · 2025
Graphs in Unsupervised Machine Learning - Lisbeth Haydee Rivera Betancur · University of Las Palmas de Gran Canaria · PhD · 2025
Contribuciones al ahorro de energía en redes de sensores inalámbricos aplicados a la piscicultura - Jordan Taylor Coscia · Virginia Tech · Doctorate · 2025
Floristics, Conservation, and Restoration of Virginia's Piedmont Grasslands - Da Kuang · Penn · PhD · 2025
Reconstructing Cell Lineage Trees from Phenotypic Features with Metric Learning - Rachael Thompson Panik · Georgia Tech · PhD · 2024
Exposure and Attitudes: Informing Upstream Barriers to Safe Systems via Machine Learning, Bayesian Estimation, and Attitudinal Research
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