Construction and analysis of clustering algorithms based on fuzzy relations and their applications to EEG data

In this work, fundamentally two algorithms have been proposed. The first one is the NRFJP (Noise-Robust FJP) algorithm which is a robust version of the known fuzzy neighborhood-based FJP (Fuzzy Joint Points) clustering algorithm. In the NRFJP algorithm each point for which certain eps1 fuzzy neighborhood cardinality is smaller than certain eps2 threshold is perceived as noise. Moreover, in case eps2 is zero, the sensitivity of the NRFJP through noises is turned off, consequently NRFJP Algorithm transforms into FJP algorithm.The second algorithm is the FN-DBSCAN (Fuzzy Neighborhood DBSCAN) algorithm which is a mixture of FJP and density-based DBSCAN (Density Based Spatial Clustering Applications with Noise) algorithms. In the study, the effects of fuzzy neighborhood relation in density-based clustering have been investigated. Besides being a more general algorithm, the FN-DBSCAN algorithm transforms into the DBSCAN algorithm when the crisp neighborhood function is used.The modified version of the FN-DBSCAN algorithm has been developed so as to apply cluster analysis to BIS data. As a result of the computational experiments, it has been observed that FN-DBSCAN based approach gives closer results to the expert?s opinion than the well-known FCM (Fuzzy c-means) clustering algorithm.The codes for the proposed algorithms, NRFJP, FN-DBSCAN and the modified version of FN-DBSCAN to analyze BIS data, have been developed in Borland C++ Builder SDK and they have been designed as an integrated software system.