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Fast and Effective Active Clustering Ensemble Based on Density Peak

机译：基于密度峰值的快速有效的活动聚类集群

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摘要

Semisupervised clustering methods improve performance by randomly selecting pairwise constraints, which may lead to redundancy and instability. In this context, active clustering is proposed to maximize the efficacy of annotations by effectively using pairwise constraints. However, existing methods lack an overall consideration of the querying criteria and repeatedly run semisupervised clustering to update labels. In this work, we first propose an active density peak (ADP) clustering algorithm that considers both representativeness and informativeness. Representative instances are selected to capture data patterns, while informative instances are queried to reduce the uncertainty of clustering results. Meanwhile, we design a fast-update-strategy to update labels efficiently. In addition, we propose an active clustering ensemble framework that combines local and global uncertainties to query the most ambiguous instances for better separation between the clusters. A weighted voting consensus method is introduced for better integration of clustering results. We conducted experiments by comparing our methods with state-of-the-art methods on real-world data sets. Experimental results demonstrate the effectiveness of our methods.
机译：半质化聚类方法通过随机选择成对约束来提高性能，这可能导致冗余和不稳定性。在这种情况下，提出了主动聚类，以通过有效地使用成对约束来最大化注释的功效。但是，现有方法缺乏对查询标准的总体考虑，并反复运行半质量群集以更新标签。在这项工作中，我们首先提出了一种积极的密度峰值（ADP）聚类算法，其考虑了代表性和信息。选择代表实例以捕获数据模式，而查询信息实例以降低聚类结果的不确定性。同时，我们设计快速更新策略以有效更新标签。此外，我们提出了一个有效的聚类集群集群框架，将本地和全局不确定性结合起来查询最模糊的实例，以便在集群之间更好地分离。引入了加权投票共识方法，以便更好地集成聚类结果。我们通过将我们的方法与现实世界数据集的最新方法进行比较来进行实验。实验结果表明了我们方法的有效性。

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