DAOC：大型网络的稳定聚类
DAOC: Stable Clustering of Large Networks
摘要：
聚类是许多涉及分析和探索各种数据的数据采掘系统的重要组成部分。数据多样性要求聚类算法在提供稳定（即，确定性和鲁棒性）的结果。此外，现代系统通常使用大型数据集进行操作，这隐含地限制了聚类算法的复杂性。然而，现有的聚类技术只是部分稳定的，因为它们保证了确定性或鲁棒性。为了解决这个问题，我们介绍了DAOC，一个确定性和凝聚重叠聚类算法。DAOC利用一种称为重叠分解的新技术，以确定性的方式识别细粒度的集群，捕获多个最优值。此外，它利用一种新的共识方法，相互最大增益，以确保鲁棒性，并进一步提高结果的稳定性，同时仍然能够识别微尺度集群。我们在合成网络和真实网络上的经验结果表明，DAOC产生稳定的集群，同时平均比最先进的确定性算法准确25%，而不需要任何调整。我们的方法的目标是大大简化和加快数据分析任务，包括迭代处理（需要确定性）以及数据波动（需要鲁棒性），并提供准确和可重复的结果。
英文摘要：
Clustering is a crucial component of many data mining systems involving the analysis and exploration of various data. Data diversity calls for clustering algorithms to be accurate while providing stable (i.e., deterministic and robust) results on arbitrary input networks. Moreover, modern systems often operate with large datasets, which implicitly constrains the complexity of the clustering algorithm. Existing clustering techniques are only partially stable, however, as they guarantee either determinism or robustness. To address this issue, we introduce DAOC, a Deterministic and Agglomerative Overlapping Clustering algorithm. DAOC leverages a new technique called Overlap Decomposition to identify fine-grained clusters in a deterministic way capturing multiple optima. In addition, it leverages a novel consensus approach, Mutual Maximal Gain, to ensure robustness and further improve the stability of the results while still being capable of identifying micro-scale clusters. Our empirical results on both synthetic and real-world networks show that DAOC yields stable clusters while being on average 25% more accurate than state-of-the-art deterministic algorithms without requiring any tuning. Our approach has the ambition to greatly simplify and speed up data analysis tasks involving iterative processing (need for determinism) as well as data fluctuations (need for robustness) and to provide accurate and reproducible results.
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。
