LSROM: Learning Self-Refined Organizing Map for Fast Imbalanced
  Streaming Data Clustering
Yongqi Xu Yujian Lee Rong Zou Yiqun Zhang Yiu-Ming Cheung
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.