Detection of BIS stage levels via fuzzy clustering approach

Abstract

In this study, FCM (Fuzzy c-Means) and FN-DBSCAN (Fuzzy Neighborhood DBSCAN) based algorithms are handled in order to use clustering methods in the determination of the stage values of BIS series data. The FN-DBSCAN algorithm is advantageous in such a way that it integrates the speed of the well-known DBSCAN (Density Based Spatial Clustering of Applications with Noise) and the robustness of the NRFJP (Noise-Robust Fuzzy Joint Points) algorithms. Such a property provides an advantage also in the detection of stable interval epochs. As a result of the computational experiments, we can conclude that FN-DBSCAN-based algorithm gives more realistic results than the FCM-based algorithm to recognize the stable duration intervals and the BIS stages in the measurement series.