论文标题
最近的邻居的自然分层聚类分析，具有接近线性的时间复杂性
Natural Hierarchical Cluster Analysis by Nearest Neighbors with Near-Linear Time Complexity
论文作者
论文摘要
我们提出了一种最接近的基于邻居的聚类算法，该算法会导致簇的自然定义层次结构。与团聚和分裂的层次结构聚类算法相反，我们的方法不依赖于算法的迭代工作，从某种意义上说，层次群的分区纯粹根据输入数据集纯粹定义。我们的方法是一种通用的层次聚类方法，因为它可以作为自下而上的或上自上而下的版本实现，这两者都会导致相同的群集。我们表明，对于某些类型的数据集，我们的算法具有接近线性的时间和空间复杂性。
We propose a nearest neighbor based clustering algorithm that results in a naturally defined hierarchy of clusters. In contrast to the agglomerative and divisive hierarchical clustering algorithms, our approach is not dependent on the iterative working of the algorithm, in the sense that the partitions of the hierarchical clusters are purely defined in accordance with the input dataset. Our method is a universal hierarchical clustering approach since it can be implemented as bottom up or top down versions, both of which result in the same clustering. We show that for certain types of datasets, our algorithm has near-linear time and space complexity.
