# Data clustering: a fundamental method in data science and management

arXiv: https://arxiv.org/abs/2412.18760 · HTML: https://mlsanity.com/paper/2412.18760/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 209 references that are in the map.
- First posted on arXiv 25 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Contrastive Clustering](https://mlsanity.com/paper/2009.09687/) (2020)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [SPICE: Semantic Pseudo-labeling for Image Clustering](https://mlsanity.com/paper/2103.09382/) (2021)
- [Clustering and Community Detection in Directed Networks: A Survey](https://mlsanity.com/paper/1308.0971/) (2013)
- [Interpreting the Curse of Dimensionality from Distance Concentration and Manifold Effect](https://mlsanity.com/paper/2401.00422/) (2023)
- [DeepVATS: Deep Visual Analytics for Time Series](https://mlsanity.com/paper/2302.03858/) (2023)
- [Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology](https://mlsanity.com/paper/2305.16656/) (2023)
- [Categorical data clustering: 25 years beyond K-modes](https://mlsanity.com/paper/2408.17244/) (2024)
- [A Wasserstein distance-based spectral clustering method for transaction data analysis](https://mlsanity.com/paper/2203.02709/) (2022)
- [DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation](https://mlsanity.com/paper/2205.08356/) (2022)
- [Analyzing categorical time series with the R package ctsfeatures](https://mlsanity.com/paper/2304.12332/) (2023)

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

- [An upper bound on the silhouette evaluation metric for clustering](https://mlsanity.com/paper/2509.08625/) (2025)
- [Contrastive Conformal Sets](https://mlsanity.com/paper/2603.26261/) (2026)

## Abstract (arXiv)

This paper explores the critical role of data clustering in data science, emphasizing its methodologies, tools, and diverse applications. Traditional techniques, such as partitional and hierarchical clustering, are analyzed alongside advanced approaches such as data stream, density-based, graph-based, and model-based clustering for handling complex structured datasets. The paper highlights key principles underpinning clustering, outlines widely used tools and frameworks, introduces the workflow of clustering in data science, discusses challenges in practical implementation, and examines various applications of clustering. By focusing on these foundations and applications, the discussion underscores clustering's transformative potential. The paper concludes with insights into future research directions, emphasizing clustering's role in driving innovation and enabling data-driven decision-making.