The diagnosis and treatment of epilepsy is a significant direction for both machine learning and brain science. This paper newly proposes a fast enhanced exemplar-based clustering (FEEC) method for incomplete EEG signal. The algorithm first compresses the potential exemplar list and reduces the pairwise similarity matrix. By processing the most complete data in the first stage, FEEC then extends the few incomplete data into the exemplar list. A new compressed similarity matrix will be constructed and the scale of this matrix is greatly reduced. Finally, FEEC optimizes the new target function by the enhanced -expansion move method. On the other hand, due to the pairwise relationship, FEEC also improves the generalization of this algorithm. In contrast to other exemplar-based models, the performance of the proposed clustering algorithm is comprehensively verified by the experiments on two datasets.

中文翻译：

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针对不完整EEG信号的快速增强的基于示例的聚类。

癫痫的诊断和治疗是机器学习和脑科学的重要方向。本文针对不完整的脑电信号提出了一种快速的基于样本的聚类方法。该算法首先压缩潜在的示例列表，并缩小成对相似性矩阵。通过在第一阶段处理最完整的数据，FEEC然后将一些不完整的数据扩展到示例列表中。将构建一个新的压缩相似度矩阵，该矩阵的规模将大大减小。最后，FEEC通过增强功能来优化新的目标功能-扩展移动方法。另一方面，由于成对关系，FEEC还改善了该算法的泛化性。与其他基于示例的模型相比，通过对两个数据集进行的实验全面验证了所提出的聚类算法的性能。