Streaming data clustering is a popular research topic in the fields of data
mining and machine learning. Compared to static data, streaming data, which is
usually analyzed in data chunks, is more susceptible to encountering the
dynamic cluster imbalanced issue. That is, the imbalanced degree of clusters
varies in different streaming data chunks, leading to corruption in either the
accuracy or the efficiency of streaming data analysis based on existing
clustering methods. Therefore, we propose an efficient approach called Learning
Self-Refined Organizing Map (LSROM) to handle the imbalanced streaming data
clustering problem, where we propose an advanced SOM for representing the
global data distribution. The constructed SOM is first refined for guiding the
partition of the dataset to form many micro-clusters to avoid the missing small
clusters in imbalanced data. Then an efficient merging of the micro-clusters is
conducted through quick retrieval based on the SOM, which can automatically
yield a true number of imbalanced clusters. In comparison to existing
imbalanced data clustering approaches, LSROM is with a lower time complexity
$O(n\log n)$, while achieving very competitive clustering accuracy. Moreover,
LSROM is interpretable and insensitive to hyper-parameters. Extensive
experiments have verified its efficacy.

提出了一种名为学习自我优化组织图（LSROM）的高效方法来处理不平衡的流数据聚类问题，该方法通过构建和细化自组织映射（SOM）来表示全局数据分布，并通过快速检索进行高效的微簇合并，以获得正确的不平衡聚类数，并且时间复杂度低，准确性竞争力强。

LSROM：用于快速非平衡流数据聚类的学习自我改进组织映射

LSROM: Learning Self-Refined Organizing Map for Fast Imbalanced  Streaming Data Clustering

Deep clustering, which learns representation and semantic clustering without
labels information, poses a great challenge for deep learning-based approaches.
Despite significant progress in recent years, most existing methods focus on
uniformly distributed datasets, significantly limiting the practical
applicability of their methods. In this paper, we propose a more practical
problem setting named deep imbalanced clustering, where the underlying classes
exhibit an imbalance distribution. To address this challenge, we introduce a
novel optimal transport-based pseudo-label learning framework. Our framework
formulates pseudo-label generation as a Semantic-regularized Progressive
Partial Optimal Transport (SP$^2$OT) problem, which progressively transports
each sample to imbalanced clusters under several prior distribution and
semantic relation constraints, thus generating high-quality and imbalance-aware
pseudo-labels. To solve SP$^2$OT, we develop a Majorization-Minimization-based
optimization algorithm. To be more precise, we employ the strategy of
majorization to reformulate the SP$^2$OT problem into a Progressive Partial
Optimal Transport problem, which can be transformed into an unbalanced optimal
transport problem with augmented constraints and can be solved efficiently by a
fast matrix scaling algorithm. Experiments on various datasets, including a
human-curated long-tailed CIFAR100, challenging ImageNet-R, and large-scale
subsets of fine-grained iNaturalist2018 datasets, demonstrate the superiority
of our method.

我们提出了一个基于最优传输的伪标签学习框架来解决深度不平衡聚类问题，该框架通过逐步将样本转移到不平衡的聚类中生成高质量且注意不平衡性的伪标签。