Research Article Fast Enhanced Exemplar-Based Clustering for Incomplete EEG Signals Anqi Bi, Wenhao Ying, and Lu Zhao School of Computer Science and Engineering, Changshu Institute of Technology, Changshu, Jiangsu, China Correspondence should be addressed to Lu Zhao; constance@cslg.edu.cn Received 16 January 2020; Accepted 27 February 2020; Published 8 May 2020 Guest Editor: Chenxi Huang Copyright©2020AnqiBietal.isisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. e diagnosis and treatment of epilepsy is a significant direction for both machine learning and brain science. is paper newly proposes a fast enhanced exemplar-based clustering (FEEC) method for incomplete EEG signal. e 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 α-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. 1. Introduction Epilepsy is a common disease of nervous system, which is characterized by sudden brain dysfunction. Although there are many other neuroimaging modalities for the recognition of brain activity, EEG signals have a high temporal reso- lution which is up to the millisecond level, and its acquisition equipment is inexpensive, portable, and noninvasive. Nowadays, most diagnoses of epilepsy are based on clinical experience and the analysis of electroencephalogram (EEG) signals. Compared with manual diagnostic method, machine learning methods are less time-consuming and more con- sistent [1–6]. Specifically, many machine learning methods such as support vector learning [7, 8], Takagi–Sugeno–Kang (TSK) fuzzy system [9, 10], and Na¨ ıve Bayes [11] have been applied. As we know that brain activity is a nonlinear, unstable complex network system, EEG signals we usually get are complicated. at is to say, some EEG signals are complete while others may miss some features, namely, incomplete. erefore, recognition of epilepsy based on machine learning models will be more promising compared with clinical diagnosis depending on experience. Moreover, EEG signals have the characteristics of high dimension and stochasticity which limit the performance of most existing clustering models, such as k-means [11] and fuzzy c mean (fcm) [12]. K-means and fcm clustering models need to preset the number of clusters in advance. More specifically, the performance of the k-means model relies on the ini- tialization of data, while the fcm model requires high in- terpretability. us, we focus on the exemplar-based clustering model [13] which is proposed by Frey in this paper. e exemplar-based clustering model has the ad- vantages of automatically obtaining the cluster number, high efficiency, and not relying on the initialization of data. In conclusion, we consider the scenario of EEG signals consisting of most complete data and few incomplete data in this paper, as shown in Figure 1. Based on the previous work about the recognition of epileptic signals, we propose a novel fast enhanced exemplar-based clustering (FEEC) model for incomplete EEG signals. As shown in Figure 1, different from existing exemplar-based clustering models, FEEC compresses the exemplar list and reduces the pairwise similarity matrix, and then FEEC optimizes the target model by the enhanced α-expansion move framework. Moreover, the contributions of this paper can be highlighted as follows: Hindawi Computational and Mathematical Methods in Medicine Volume 2020, Article ID 4147807, 11 pages https://doi.org/10.1155/2020/4147807