# A Comparison of Methods for Player Clustering via Behavioral Telemetry

arXiv: https://arxiv.org/abs/1407.3950 · HTML: https://mlsanity.com/paper/1407.3950/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2014.

## Built on by (most cited first, 6 of 6)

- [Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2](https://mlsanity.com/paper/2210.09028/) (2022)
- [Open Player Modeling: Empowering Players through Data Transparency](https://mlsanity.com/paper/2110.05810/) (2021)
- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [JSON-Bag: A generic game trajectory representation](https://mlsanity.com/paper/2508.00712/) (2025)
- [Spiders Based on Anxiety: How Reinforcement Learning Can Deliver Desired User Experience in Virtual Reality Personalized Arachnophobia Treatment](https://mlsanity.com/paper/2409.17406/) (2024)
- [Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning](https://mlsanity.com/paper/2510.10263/) (2025)

## Abstract (arXiv)

The analysis of user behavior in digital games has been aided by the introduction of user telemetry in game development, which provides unprecedented access to quantitative data on user behavior from the installed game clients of the entire population of players. Player behavior telemetry datasets can be exceptionally complex, with features recorded for a varying population of users over a temporal segment that can reach years in duration. Categorization of behaviors, whether through descriptive methods (e.g. segmention) or unsupervised/supervised learning techniques, is valuable for finding patterns in the behavioral data, and developing profiles that are actionable to game developers. There are numerous methods for unsupervised clustering of user behavior, e.g. k-means/c-means, Non-negative Matrix Factorization, or Principal Component Analysis. Although all yield behavior categorizations, interpretation of the resulting categories in terms of actual play behavior can be difficult if not impossible. In this paper, a range of unsupervised techniques are applied together with Archetypal Analysis to develop behavioral clusters from playtime data of 70,014 World of Warcraft players, covering a five year interval. The techniques are evaluated with respect to their ability to develop actionable behavioral profiles from the dataset.