SCE：一种流形正则化的集合覆盖数据划分方法
SCE: A manifold regularized set-covering method for data partitioning
摘要：
聚类分析在数据分析中起着非常重要的作用。近年来，聚类集成作为一种聚类分析工具，因其稳健性、稳定性和准确性而受到广泛关注。人们已经做了很多努力，将不同的初始聚类结果组合成一个具有更好性能的聚类解决方案。然而，他们忽略了在执行聚类集成的原始数据的结构信息。在本文中，我们提出了一个结构集群包围（SCE）算法的数据划分制定为一个集覆盖问题。特别地，我们构造了一个拉普拉斯正则化的目标函数来捕获聚类之间的结构信息。此外，考虑到在初始聚类结果的歧视性信息的重要性，我们添加了歧视性的约束到我们提出的目标函数。最后，我们验证了SCE算法在合成和真实数据集上的性能。实验结果表明了SCE算法的有效性。
英文摘要：
Cluster analysis plays a very important role in data analysis. In these years, cluster ensemble, as a cluster analysis tool, has drawn much attention for its robustness, stability, and accuracy. Many efforts have been done to combine different initial clustering results into a single clustering solution with better performance. However, they neglect the structure information of the raw data in performing the cluster ensemble. In this paper, we propose a Structural Cluster Ensemble (SCE) algorithm for data partitioning formulated as a set-covering problem. In particular, we construct a Laplacian regularized objective function to capture the structure information among clusters. Moreover, considering the importance of the discriminative information underlying in the initial clustering results, we add a discriminative constraint into our proposed objective function. Finally, we verify the performance of the SCE algorithm on both synthetic and real data sets. The experimental results show the effectiveness of our proposed method SCE algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
