# TANGO: Clustering with Typicality-Aware Nonlocal Mode-Seeking and Graph-Cut Optimization

arXiv: https://arxiv.org/abs/2408.10084 · HTML: https://mlsanity.com/paper/2408.10084/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 57 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Ultra-Scalable Spectral Clustering and Ensemble Clustering](https://mlsanity.com/paper/1903.01057/) (2019)
- [Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms](https://mlsanity.com/paper/1712.06206/) (2017)
- [Quickshift++: Provably Good Initializations for Sample-Based Mean Shift](https://mlsanity.com/paper/1805.07909/) (2018)
- [A Tighter Analysis of Spectral Clustering, and Beyond](https://mlsanity.com/paper/2208.01724/) (2022)
- [Improved Analysis of Spectral Algorithm for Clustering](https://mlsanity.com/paper/1912.02997/) (2019)
- [On the Consistency of Quick Shift](https://mlsanity.com/paper/1710.10646/) (2017)
- [Fast and Simple Spectral Clustering in Theory and Practice](https://mlsanity.com/paper/2310.10939/) (2023)
- [Modal-set estimation with an application to clustering](https://mlsanity.com/paper/1606.04166/) (2016)
- [VDPC: Variational Density Peak Clustering Algorithm](https://mlsanity.com/paper/2201.00641/) (2021)
- [Mean Field Analysis of Personalized PageRank with Implications for Local Graph Clustering](https://mlsanity.com/paper/1806.07640/) (2018)

## Built on by (most cited first, 1 of 1)

- [ARMA-C3: A Contrastive ARMA Convolutional Framework for Unsupervised and Semi-supervised Classification](https://mlsanity.com/paper/2605.25657/) (2026)

## Abstract (arXiv)

Density-based mode-seeking methods generate a density-ascending dependency from low-density points towards higher-density neighbors. Current mode-seeking methods identify modes by breaking some dependency connections, but relying heavily on local data characteristics, requiring case-by-case threshold settings or human intervention to be effective for different datasets. To address this issue, we introduce a novel concept called typicality, by exploring the locally defined dependency from a global perspective, to quantify how confident a point would be a mode. We devise an algorithm that effectively and efficiently identifies modes with the help of the global-view typicality. To implement and validate our idea, we design a clustering method called TANGO, which not only leverages typicality to detect modes, but also utilizes graph-cut with an improved path-based similarity to aggregate data into the final clusters. Moreover, this paper also provides some theoretical analysis on the proposed algorithm. Experimental results on several synthetic and extensive real-world datasets demonstrate the effectiveness and superiority of TANGO. The code is available at https://github.com/SWJTU-ML/TANGOcode.