# On the Use of Relative Validity Indices for Comparing Clustering Approaches

arXiv: https://arxiv.org/abs/2404.10351 · HTML: https://mlsanity.com/paper/2404.10351/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 203 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 20 of 20)

- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Deep Spectral Clustering using Dual Autoencoder Network](https://mlsanity.com/paper/1904.13113/) (2019)
- [Conditional GAN for timeseries generation](https://mlsanity.com/paper/2006.16477/) (2020)
- [Clustering Algorithms: A Comparative Approach](https://mlsanity.com/paper/1612.08388/) (2016)
- [A Benchmark Study on Time Series Clustering](https://mlsanity.com/paper/2004.09546/) (2020)
- [Are Cluster Validity Measures (In)valid?](https://mlsanity.com/paper/2208.01261/) (2022)
- [A framework for benchmarking clustering algorithms](https://mlsanity.com/paper/2209.09493/) (2022)
- [Influential Feature PCA for high dimensional clustering](https://mlsanity.com/paper/1407.5241/) (2014)
- [A survey on trajectory clustering analysis](https://mlsanity.com/paper/1802.06971/) (2018)
- [Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm](https://mlsanity.com/paper/2209.05757/) (2022)
- [Clustering performance analysis using a new correlation-based cluster validity index](https://mlsanity.com/paper/2109.11172/) (2021)
- [The Area Under the ROC Curve as a Measure of Clustering Quality](https://mlsanity.com/paper/2009.02400/) (2020)
- [Shape-Based Approach to Household Load Curve Clustering and Prediction](https://mlsanity.com/paper/1702.01414/) (2017)
- [Characterizing and comparing external measures for the assessment of cluster analysis and community detection](https://mlsanity.com/paper/2102.00708/) (2021)
- [A new measure for assessment of clustering based on kernel density estimation](https://mlsanity.com/paper/2201.02030/) (2022)
- [Benchmarking distance-based partitioning methods for mixed-type data](https://mlsanity.com/paper/2203.16287/) (2022)
- [Review of Clustering Methods for Functional Data](https://mlsanity.com/paper/2210.00847/) (2022)
- [Clustering strategy and method selection](https://mlsanity.com/paper/1503.02059/) (2015)
- [Cluster validation by measurement of clustering characteristics relevant to the user](https://mlsanity.com/paper/1703.09282/) (2017)
- [Synthetic Time-Series Load Data via Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2107.03545/) (2021)

## Built on by (most cited first, 2 of 2)

- [CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series Clustering](https://mlsanity.com/paper/2505.14596/) (2025)
- [CROCS: A Two-Stage Clustering Framework for Behaviour-Centric Consumer Segmentation with Smart Meter Data](https://mlsanity.com/paper/2601.10494/) (2026)

## Abstract (arXiv)

Relative Validity Indices (RVIs) such as the Silhouette Width Criterion and Davies Bouldin indices are the most widely used tools for evaluating and optimising clustering outcomes. Traditionally, their ability to rank collections of candidate dataset partitions has been used to guide the selection of the number of clusters, and to compare partitions from different clustering algorithms. However, there is a growing trend in the literature to use RVIs when selecting a Similarity Paradigm (SP) for clustering - the combination of normalisation procedure, representation method, and distance measure which affects the computation of object dissimilarities used in clustering. Despite the growing prevalence of this practice, there has been no empirical or theoretical investigation into the suitability of RVIs for this purpose. Moreover, since RVIs are computed using object dissimilarities, it remains unclear how they would need to be implemented for fair comparisons of different SPs. This study presents the first comprehensive investigation into the reliability of RVIs for SP selection. We conducted extensive experiments with seven popular RVIs on over 2.7 million clustering partitions of synthetic and real-world datasets, encompassing feature-vector and time-series data. We identified fundamental conceptual limitations undermining the use of RVIs for SP selection, and our empirical findings confirmed this predicted unsuitability. Among our recommendations, we suggest instead that practitioners select SPs by using external validation on high quality labelled datasets or carefully designed outcome-oriented objective criteria, both of which should be informed by careful consideration of dataset characteristics, and domain requirements. Our findings have important implications for clustering methodology and evaluation, suggesting the need for more rigorous approaches to SP selection.