Name
Title | ||
|---|---|---|
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 multi-view learning mechanism combines information from each view to improve the algorithms 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2947689 | IEEE ACCESS |
Keywords | DocType | Volume |
Clustering algorithms, Electroencephalography, Epilepsy, Periodic structures, Learning systems, Classification algorithms, Neural networks, Epileptic EEG, multi-view, multi-medoid, fuzzy clustering | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qianyi Zhan | 1 | 1 | 2.04 |
| Yizhang Jiang | 2 | 382 | 27.24 |
| Kaijian Xia | 3 | 0 | 0.34 |
| Jing Xue | 4 | 10 | 3.14 |
| Wei Hu | 5 | 0 | 0.34 |
| Huangxing Lin | 6 | 0 | 0.34 |
| Yuan Liu | 7 | 0 | 0.34 |