Efficient Topology-Driven Clustering for Imbalanced Streaming Biomedical Data Analysis

Clustering drifting data is common in the field of biomedical data analysis. Data chunks collected at different periods often exhibit clusters with significantly different sizes, and drifting distributions of clusters also appear frequently. We call such composite phenomenon imbalance-drifting, which can severely impact the accuracy and efficiency of cluster analysis. Therefore, we propose a topology-representation-based clustering paradigm, which first learns an informative global data representation in a self-organizing manner to obtain a map with nested representative data points. Then fast and accurate clustering is facilitated by quickly retrieving similar data points according to the topology. As the constructed Self-Organizing Map (SOM) is exploited for informative representation, micro partition, and quick merging, to achieve advanced clustering under imbalance-drifting, the proposed approach is thus called Tri-Squeezing SOM for Clustering (TSSC). It turns out that TSSC significantly reduces the time complexity for clustering an n-scale imbalance-streaming data without sacrificing accuracy. Moreover, TSSC can automatically determine the number of clusters k, and features interpretability and hyper-parameter robustness. Extensive results on both biomedical datasets and synthetic datasets verify the superiority of TSSC.