# Survey of state-of-the-art mixed data clustering algorithms

arXiv: https://arxiv.org/abs/1811.04364 · HTML: https://mlsanity.com/paper/1811.04364/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2018.

## Built on by (most cited first, 13 of 13)

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- [A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data](https://mlsanity.com/paper/2407.03389/) (2024)
- [Mixed Data Clustering Survey and Challenges](https://mlsanity.com/paper/2512.03070/) (2025)
- [Gower's similarity coefficients with automatic weight selection](https://mlsanity.com/paper/2401.17041/) (2024)
- [Learning Ensembles of Interpretable Simple Structure](https://mlsanity.com/paper/2502.19602/) (2025)
- [MMM: Clustering Multivariate Longitudinal Mixed-type Data](https://mlsanity.com/paper/2509.12166/) (2025)
- [Clustering Approaches for Mixed-Type Data: A Comparative Study](https://mlsanity.com/paper/2511.19755/) (2025)
- [PretopoMD: Pretopology-based Mixed Data Hierarchical Clustering](https://mlsanity.com/paper/2512.03071/) (2025)

## Abstract (arXiv)

Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied to mixed datasets to find structures and to group similar objects for further analysis. However, clustering mixed data is challenging because it is difficult to directly apply mathematical operations, such as summation or averaging, to the feature values of these datasets. In this paper, we present a taxonomy for the study of mixed data clustering algorithms by identifying five major research themes. We then present a state-of-the-art review of the research works within each research theme. We analyze the strengths and weaknesses of these methods with pointers for future research directions. Lastly, we present an in-depth analysis of the overall challenges in this field, highlight open research questions and discuss guidelines to make progress in the field.