Shi Yifan, Yu Zhiwen, Cao Wenming, Chen C L Philip, Wong Hau-San, Han Guoqiang
IEEE Trans Neural Netw Learn Syst. 2021 Aug;32(8):3593-3607. doi: 10.1109/TNNLS.2020.3015795. Epub 2021 Aug 3.
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.
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性。在此背景下，提出了主动聚类，以通过有效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整体考虑，并且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主动密度峰值（ADP）聚类算法，该算法同时考虑了代表性和信息量。选择代表性实例以捕获数据模式，同时查询信息量丰富的实例以减少聚类结果的不确定性。同时，我们设计了一种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，该框架结合了局部和全局不确定性，以查询最模糊的实例，以便在聚类之间实现更好的分离。引入了加权投票共识方法以更好地整合聚类结果。我们通过在真实数据集上与现有方法比较我们的方法进行了实验。实验结果证明了我们方法的有效性。
