# Association and Consolidation: Evolutionary Memory-Enhanced Incremental Multi-View Clustering

arXiv: https://arxiv.org/abs/2509.14544 · HTML: https://mlsanity.com/paper/2509.14544/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 18 Sep 2025.

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

- [Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System](https://mlsanity.com/paper/2201.12604/) (2022)
- [Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm](https://mlsanity.com/paper/1510.06895/) (2015)
- [Auto-weighted Multi-view Clustering for Large-scale Data](https://mlsanity.com/paper/2303.01983/) (2023)
- [Fast Continual Multi-View Clustering with Incomplete Views](https://mlsanity.com/paper/2306.02389/) (2023)
- [Unpaired Multi-View Graph Clustering with Cross-View Structure Matching](https://mlsanity.com/paper/2307.03476/) (2023)
- [Subspace-Contrastive Multi-View Clustering](https://mlsanity.com/paper/2210.06795/) (2022)
- [S^2MVTC: a Simple yet Efficient Scalable Multi-View Tensor Clustering](https://mlsanity.com/paper/2403.09107/) (2024)
- [OpenViewer: Openness-Aware Multi-View Learning](https://mlsanity.com/paper/2412.12596/) (2024)
- [Live and Learn: Continual Action Clustering with Incremental Views](https://mlsanity.com/paper/2404.07962/) (2024)
- [Multi-View Incremental Learning with Structured Hebbian Plasticity for Enhanced Fusion Efficiency](https://mlsanity.com/paper/2412.12801/) (2024)

## Abstract (arXiv)

Incremental multi-view clustering aims to achieve stable clustering results while addressing the stability-plasticity dilemma (SPD) in view-incremental scenarios. The core challenge is that the model must have enough plasticity to quickly adapt to new data, while maintaining sufficient stability to consolidate long-term knowledge. To address this challenge, we propose a novel Evolutionary Memory-Enhanced Incremental Multi-View Clustering (EMIMC), inspired by the memory regulation mechanisms of the human brain. Specifically, we design a rapid association module to establish connections between new and historical views, thereby ensuring the plasticity required for learning new knowledge. Second, a cognitive forgetting module with a decay mechanism is introduced. By dynamically adjusting the contribution of the historical view to optimize knowledge integration. Finally, we propose a knowledge consolidation module to progressively refine short-term knowledge into stable long-term memory using temporal tensors, thereby ensuring model stability. By integrating these modules, EMIMC achieves strong knowledge retention capabilities in scenarios with growing views. Extensive experiments demonstrate that EMIMC exhibits remarkable advantages over existing state-of-the-art methods.