基于最近邻平衡的聚类算法，自动确定最优聚类数。
Nearest Neighbour Equilibrium Clustering
- 以邻居间平衡状态定义簇，兼顾大小与紧密度
- 点对簇的对齐强度可量化，用于自动选最佳簇数
- 无需人工干预，效率高且结果优于主流方法
提出一种新颖直观的基于最近邻的聚类算法，簇的定义基于大小与凝聚性之间的平衡条件。该平衡条件可量化每个点对簇的对齐强度，进而自然导出模型选择准则，使方法完全自动化。算法实现简单、计算高效，在与文献中相关基准对比时，聚类质量显著更优。R语言实现代码可在 https://github.com/DavidHofmeyr/NNEC 获取。
原文摘要 · Abstract (English)
A novel and intuitive nearest neighbours based clustering algorithm is introduced, in which a cluster is defined in terms of an equilibrium condition which balances its size and cohesiveness. The formulation of the equilibrium condition allows for a quantification of the strength of alignment of each point to a cluster, with these cluster alignment strengths leading naturally to a model selection criterion which renders the proposed approach fully automatable. The algorithm is simple to implement and computationally efficient, and produces clustering solutions of extremely high quality in comparison with relevant benchmarks from the literature. R code to implement the approach is available from https://github.com/DavidHofmeyr/NNEC.
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