Robust Recursive Fuzzy Clustering-Based Segmentation of Biological Time Series

The problem of adaptive segmentation of time series changing their properties at a priori unknown moments is considered. The proposed approach is based on the idea of indirect sequence clustering which is realized with a novel robust recursive fuzzy clustering algorithm that can process incoming observations online, and is stable with respect to outliers that are often present in real data. An application to the segmentation of a biological time series confirms the efficiency of the proposed algorithm