Epileptic EEG Detection Using a Multi-View Fuzzy Clustering Algorithm with Multi-Medoid

Abstract

Using clustering algorithms to automatically analyze EEGs of patients and to identify the characteristic waves of epilepsy is of high clinical value. Traditional clustering algorithms mostly use a calculated virtual single representative medoid point to describe the cluster structure, but this single representative medoid point has insufficient information. To accurately capture more accurate intracluster structural information, a representative multi-medoid points strategy is adopted, which describes the cluster structure by assigning representative weights to each sample in the cluster. Considering that the multiview learning mechanism combines information from each view to improve the algorithm's clustering performance, a multi-view fuzzy clustering algorithm with multi-medoid (MvFMMdd) is proposed. This algorithm discards the approach of the traditional fuzzy clustering algorithm, which uses a single virtual representative point to characterize the cluster structure, and uses several real representative points to describe the cluster structure. Experiments verify the medical significance of the proposed algorithm. Epileptic EEG, multi-view, multi-medoid, fuzzy clustering. INDEX TERMS

An Improved K-Means Algorithm Based on Evidence Distance

“…Depending on the clustering method and characteristics, clustering algorithms can be classified as: divisional, hierarchical, density algorithms, graph theoretic clustering, grid algorithms, model algorithms, etc. [15,16].…”