# Clustering Algorithms: A Comparative Approach

arXiv: https://arxiv.org/abs/1612.08388 · HTML: https://mlsanity.com/paper/1612.08388/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2016.

## Built on by (most cited first, 28 of 28)

- [Machine Learning in Aerodynamic Shape Optimization](https://mlsanity.com/paper/2202.07141/) (2022)
- [From A-to-Z Review of Clustering Validation Indices](https://mlsanity.com/paper/2407.20246/) (2024)
- [A Rapid Review of Clustering Algorithms](https://mlsanity.com/paper/2401.07389/) (2024)
- [Multi-Source Data Fusion for Cyberattack Detection in Power Systems](https://mlsanity.com/paper/2101.06897/) (2021)
- [The Impact of Feature Scaling In Machine Learning: Effects on Regression and Classification Tasks](https://mlsanity.com/paper/2506.08274/) (2025)
- [Human-interpretable clustering of short-text using large language models](https://mlsanity.com/paper/2405.07278/) (2024)
- [HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis](https://mlsanity.com/paper/2102.06940/) (2021)
- [Grain segmentation in atomistic simulations using orientation-based iterative self-organizing data analysis](https://mlsanity.com/paper/2112.03348/) (2021)
- [Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learning](https://mlsanity.com/paper/2304.09154/) (2023)
- [On the Use of Relative Validity Indices for Comparing Clustering Approaches](https://mlsanity.com/paper/2404.10351/) (2024)
- [When to Accept Automated Predictions and When to Defer to Human Judgment?](https://mlsanity.com/paper/2407.07821/) (2024)
- [On predicting research grants productivity](https://mlsanity.com/paper/2106.10700/) (2021)
- [J-Score: A Robust Measure of Clustering Accuracy](https://mlsanity.com/paper/2109.01306/) (2021)
- [Feature extraction using Spectral Clustering for Gene Function Prediction using Hierarchical Multi-label Classification](https://mlsanity.com/paper/2203.13551/) (2022)
- [Superclustering by finding statistically significant separable groups of optimal gaussian clusters](https://mlsanity.com/paper/2309.02623/) (2023)
- [Assessing the impact of dimensionality reduction on clustering performance - a systematic study](https://mlsanity.com/paper/2604.22099/) (2026)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)
- [Application of the Affinity Propagation Clustering Technique to obtain traffic accident clusters at macro, meso, and micro levels](https://mlsanity.com/paper/2202.05175/) (2022)
- [Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models](https://mlsanity.com/paper/2205.03316/) (2022)
- [Using Full-Text Content to Characterize and Identify Best Seller Books](https://mlsanity.com/paper/2210.02334/) (2022)
- [A Survey From Distributed Machine Learning to Distributed Deep Learning](https://mlsanity.com/paper/2307.05232/) (2023)
- [Machine learning in physics: a short guide](https://mlsanity.com/paper/2310.10368/) (2023)
- [SPINEX-Clustering: Similarity-based Predictions with Explainable Neighbors Exploration for Clustering Problems](https://mlsanity.com/paper/2407.07222/) (2024)
- [ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation](https://mlsanity.com/paper/2509.25289/) (2025)
- [The elbow statistic: Multiscale clustering statistical significance](https://mlsanity.com/paper/2603.03235/) (2026)
- [Amortized Neural Clustering of Time Series based on Statistical Features](https://mlsanity.com/paper/2605.13128/) (2026)
- [Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering](https://mlsanity.com/paper/2605.19220/) (2026)
- [Interrelating Fruchterman-Reingold Graph Visualization and Agglomerative Clustering](https://mlsanity.com/paper/2609.35073/) (2026)

## Abstract (arXiv)

Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use (and understanding) of machine learning methods in practical applications becomes essential. While a myriad of classification methods have been proposed, there is no consensus on which methods are more suitable for a given dataset. As a consequence, it is important to comprehensively compare methods in many possible scenarios. In this context, we performed a systematic comparison of 7 well-known clustering methods available in the R language. In order to account for the many possible variations of data, we considered artificial datasets with several tunable properties (number of classes, separation between classes, etc). In addition, we also evaluated the sensitivity of the clustering methods with regard to their parameters configuration. The results revealed that, when considering the default configurations of the adopted methods, the spectral approach usually outperformed the other clustering algorithms. We also found that the default configuration of the adopted implementations was not accurate. In these cases, a simple approach based on random selection of parameters values proved to be a good alternative to improve the performance. All in all, the reported approach provides subsidies guiding the choice of clustering algorithms.