# Learning Self-Growth Maps for Fast and Accurate Imbalanced Streaming Data Clustering

arXiv: https://arxiv.org/abs/2404.09243 · HTML: https://mlsanity.com/paper/2404.09243/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 67 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [Data Stream Clustering: A Review](https://mlsanity.com/paper/2007.10781/) (2020)
- [Multi-Prototypes Convex Merging Based K-Means Clustering Algorithm](https://mlsanity.com/paper/2302.07045/) (2023)

## Built on by (most cited first, 9 of 9)

- [Mask the Redundancy: Evolving Masking Representation Learning for Multivariate Time-Series Clustering](https://mlsanity.com/paper/2511.17008/) (2025)
- [Categorical Data Clustering via Value Order Estimated Distance Metric Learning](https://mlsanity.com/paper/2411.15189/) (2024)
- [Hierarchical Sparse Representation Clustering for High-Dimensional Data Streams](https://mlsanity.com/paper/2409.04698/) (2024)
- [Learning Unbiased Cluster Descriptors for Interpretable Imbalanced Concept Drift Detection](https://mlsanity.com/paper/2603.06757/) (2026)
- [Hierarchical Reference Sets for Robust Unsupervised Detection of Scattered and Clustered Outliers](https://mlsanity.com/paper/2603.12847/) (2026)
- [Random Normed k-Means: A Paradigm-Shift in Clustering within Probabilistic Metric Spaces](https://mlsanity.com/paper/2504.03928/) (2025)
- [Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions](https://mlsanity.com/paper/2512.15286/) (2025)
- [One-Shot Federated Clustering of Non-Independent Completely Distributed Data](https://mlsanity.com/paper/2601.17512/) (2026)
- [Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numbers](https://mlsanity.com/paper/2603.12684/) (2026)

## Abstract (arXiv)

Streaming data clustering is a popular research topic in data mining and machine learning. Since streaming data is usually analyzed in data chunks, it is more susceptible to encounter the dynamic cluster imbalance issue. That is, the imbalance ratio of clusters changes over time, which can easily lead to fluctuations in either the accuracy or the efficiency of streaming data clustering. Therefore, we propose an accurate and efficient streaming data clustering approach to adapt the drifting and imbalanced cluster distributions. We first design a Self-Growth Map (SGM) that can automatically arrange neurons on demand according to local distribution, and thus achieve fast and incremental adaptation to the streaming distributions. Since SGM allocates an excess number of density-sensitive neurons to describe the global distribution, it can avoid missing small clusters among imbalanced distributions. We also propose a fast hierarchical merging strategy to combine the neurons that break up the relatively large clusters. It exploits the maintained SGM to quickly retrieve the intra-cluster distribution pairs for merging, which circumvents the most laborious global searching. It turns out that the proposed SGM can incrementally adapt to the distributions of new chunks, and the Self-grOwth map-guided Hierarchical merging for Imbalanced data clustering (SOHI) approach can quickly explore a true number of imbalanced clusters. Extensive experiments demonstrate that SOHI can efficiently and accurately explore cluster distributions for streaming data.