dFC: A Data-density-aware Fuzzy Clustering Algorithm for Imbalanced Biomedical Datasets

摘要

Imbalanced data are common in biomedical areas but pose a computational challenge for clustering methods. This paper investigates the effects of imbalanced datasets using fuzzy clustering, and proposes a data-density-aware fuzzy clustering method (dFC) to solve this problem. Specifically, a dataset is segmented into different areas with similar local density, and then a novel fuzzy clustering algorithm was implemented based on the initial partition. Our new method was evaluated using real and simulated imbalanced datasets. The experimental results show that our method can better classify imbalanced datasets with less iterations and computational time compared to FCM especially for large datasets.