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.

中文翻译：

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基于密度峰值的快速有效的主动聚类集成

半监督聚类方法通过随机选择成对约束来提高性能，这可能会导致冗余和不稳定。在这种情况下，主动聚类被提议通过有效地使用成对约束来最大化注释的功效。然而，现有方法缺乏对查询标准的全面考虑，并反复运行半监督聚类来更新标签。在这项工作中，我们首先提出了一种同时考虑代表性和信息量的活动密度峰值（ADP）聚类算法。选择代表性实例来捕获数据模式，同时查询信息实例以减少聚类结果的不确定性。同时，我们设计了一个快速更新策略来有效地更新标签。此外，我们提出了一个主动聚类集成框架，它结合了局部和全局不确定性来查询最模糊的实例，以便更好地分离集群。为了更好地整合聚类结果，引入了加权投票共识方法。我们通过将我们的方法与现实世界数据集上的最新方法进行比较来进行实验。实验结果证明了我们方法的有效性。