通过同步进行集群

Christian Böhm, C. Plant, Junming Shao, Qinli Yang
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引用次数: 80

摘要

同步是自然界中一个强大的基本概念,它调节着从细胞代谢到个体群体社会行为的各种复杂过程。因此,同步现象得到了广泛的研究,并提出了鲁棒捕捉动态同步过程的模型,如广泛的Kuramoto模型。受强大的同步概念的启发,我们提出了Sync,一种新颖的集群方法。其基本思想是将每个数据对象视为相位振荡器,并模拟对象随时间的交互行为。随着时间的推移,相似的对象自然地同步在一起,形成不同的集群。Sync继承自同步,具有以下几个令人满意的特性:动态同步所揭示的簇真实地反映了数据集的内在结构;Sync不依赖于任何分布假设,允许检测任意数量、形状和大小的簇。此外,同步的概念允许自然地处理离群值,因为离群值不与集群对象同步。对于全自动聚类,我们建议将同步与最小描述长度原则相结合。在合成数据和真实世界数据上进行的大量实验证明了我们方法的有效性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clustering by synchronization
Synchronization is a powerful basic concept in nature regulating a large variety of complex processes ranging from the metabolism in the cell to social behavior in groups of individuals. Therefore, synchronization phenomena have been extensively studied and models robustly capturing the dynamical synchronization process have been proposed, e.g. the Extensive Kuramoto Model. Inspired by the powerful concept of synchronization, we propose Sync, a novel approach to clustering. The basic idea is to view each data object as a phase oscillator and simulate the interaction behavior of the objects over time. As time evolves, similar objects naturally synchronize together and form distinct clusters. Inherited from synchronization, Sync has several desirable properties: The clusters revealed by dynamic synchronization truly reflect the intrinsic structure of the data set, Sync does not rely on any distribution assumption and allows detecting clusters of arbitrary number, shape and size. Moreover, the concept of synchronization allows natural outlier handling, since outliers do not synchronize with cluster objects. For fully automatic clustering, we propose to combine Sync with the Minimum Description Length principle. Extensive experiments on synthetic and real world data demonstrate the effectiveness and efficiency of our approach.
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