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