Abstract Algorithms implementing populations of agents which interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here a swarm system, called Databionic swarm (DBS), is introduced which is able to adapt itself to structures of high-dimensional data characterized by distance and/or density-based structures in the data space. By exploiting the interrelations of swarm intelligence, self-organization and emergence, DBS serves as an alternative approach to the optimization of a global objective function in the task of clustering. The swarm omits the usage of a global objective function and is parameter-free because it searches for the Nash equilibrium during its annealing process. To our knowledge, DBS is the first swarm combining these approaches. Its clustering can outperform common clustering methods such as K-means, PAM, single linkage, spectral clustering, model-based clustering, and Ward, if no prior knowledge about the data is available. A central problem in clustering is the correct estimation of the number of clusters. This is addressed by a DBS visualization called topographic map which allows assessing the number of clusters. It is known that all clustering algorithms construct clusters, irrespective of the data set contains clusters or not. In contrast to most other clustering algorithms, the topographic map identifies, that clustering of the data is meaningless if the data contains no (natural) clusters. The performance of DBS is demonstrated on a set of benchmark data, which are constructed to pose difficult clustering problems and in two real-world applications.

中文翻译：

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用于自组织聚类的群智能

摘要 实现相互交互并感知其环境的代理群体的算法可能会表现出诸如自组织和群体智能之类的紧急行为。这里引入了一个称为 Databionic swarm (DBS) 的群系统，它能够适应以数据空间中基于距离和/或基于密度的结构为特征的高维数据结构。通过利用群体智能、自组织和涌现之间的相互关系，DBS 可作为在聚类任务中优化全局目标函数的替代方法。群省略了全局目标函数的使用并且是无参数的，因为它在其退火过程中搜索纳什均衡。据我们所知，DBS 是第一个结合这些方法的群体。如果没有关于数据的先验知识，它的聚类可以胜过常用的聚类方法，如 K-means、PAM、单链接、谱聚类、基于模型的聚类和 Ward。聚类中的一个核心问题是对聚类数量的正确估计。这是通过称为地形图的 DBS 可视化来解决的，它允许评估集群的数量。众所周知，所有聚类算法都构建了簇，而不管数据集是否包含簇。与大多数其他聚类算法相比，地形图识别出，如果数据不包含（自然）聚类，则数据聚类是没有意义的。DBS 的性能在一组基准数据上得到了证明，这些数据被构造为提出困难的聚类问题，并且在两个实际应用中。