Evolutionary Active Constrained Clustering for Obstructive Sleep Apnea Analysis
Evolutionary Active Constrained Clustering for Obstructive Sleep Apnea Analysis
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DOI：
10.1007/s41019-018-0080-6
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发表时间：
2018-11
影响因子：
4.2
通讯作者：
S. T. Mai;S. Amer-Yahia;S. Bailly;J. Pépin;A. Chouakria;K. T. Nguyen;Anh-Duong Nguyen
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作者：
S. T. Mai;S. Amer-Yahia;S. Bailly;J. Pépin;A. Chouakria;K. T. Nguyen;Anh-Duong Nguyen
我们引入了一种新的交互式框架来处理实例级和时间平滑度约束，用于聚类大型纵向数据并跟踪随时间的聚类演变。它包括一个约束聚类算法，称为CVQE+，它优化了聚类质量，约束违反和连续数据快照之间的历史成本。在我们的框架的中心是一个简单而有效的主动学习技术，namedBorder，用于迭代地选择最具信息量的对象对来查询用户，并使用新的约束更新聚类。然后，这些约束在每个数据快照内部和快照之间通过两种方案（称为约束继承和约束传播）传播，以进一步增强结果。此外，在连续快照之间强制执行历史约束，以确保它们之间的结果的一致性。实验表明，更好的或可比的聚类结果比国家的最先进的技术，以及高的可扩展性，为大型数据集。最后，我们应用我们的算法对阻塞性睡眠呼吸暂停患者的表型进行聚类，并跟踪这些聚类如何随着时间的推移而演变。
We introduce a novel interactive framework to handle both instance-level and temporal smoothness constraints for clustering large longitudinal data and for tracking the cluster evolutions over time. It consists of a constrained clustering algorithm, calledCVQE+, which optimizes the clustering quality, constraint violation and the historical cost between consecutive data snapshots. At the center of our framework is a simple yet effective active learning technique, namedBorder, for iteratively selecting the most informative pairs of objects to query users about, and updating the clustering with new constraints. Those constraints are then propagated inside each data snapshot and between snapshots via two schemes, calledconstraint inheritanceandconstraint propagation, to further enhance the results. Moreover, a historical constraint is enforced between consecutive snapshots to ensure the consistency of results among them. Experiments show better or comparable clustering results than state-of-the-art techniques as well as high scalability for large datasets. Finally, we apply our algorithm for clustering phenotypes in patients with Obstructive Sleep Apnea as well as for tracking how these clusters evolve over time.