论文标题
通过扭矩平衡与质量和距离进行聚类
Clustering via torque balance with mass and distance
论文作者
论文摘要
分组相似的对象是科学分析的基本工具，在从生物学和化学到天文学和模式识别的学科中无处不在。受到星系合并时引力相互作用中存在的扭矩平衡的启发，我们提出了一种基于宇宙的两个自然特性的新型聚类方法：质量和距离。描述质量和距离相互作用的扭矩概念构成了提出的无参数聚类算法的基础，该算法利用扭矩平衡识别任何簇，无论形状，大小或密度如何。引力相互作用控制了合并过程，而扭矩平衡的概念则揭示了不符合自然秩序拆除的分区。基准数据集的实验显示了所提出算法的巨大多功能性。
Grouping similar objects is a fundamental tool of scientific analysis, ubiquitous in disciplines from biology and chemistry to astronomy and pattern recognition. Inspired by the torque balance that exists in gravitational interactions when galaxies merge, we propose a novel clustering method based on two natural properties of the universe: mass and distance. The concept of torque describing the interactions of mass and distance forms the basis of the proposed parameter-free clustering algorithm, which harnesses torque balance to recognize any cluster, regardless of shape, size, or density. The gravitational interactions govern the merger process, while the concept of torque balance reveals partitions that do not conform to the natural order for removal. Experiments on benchmark data sets show the enormous versatility of the proposed algorithm.
