一种去除聚类数量和邻域参数设置的自适应聚类算法

传统聚类方法往往无法避免邻域参数和聚类数量的选择问题,而这些参数在不同形状的数据中的最优选择也不尽相同,需要根据大量先验知识确定合适的参数选择范围.针对上述参数选择问题,提出了一种基于自然邻居思想的边界剥离聚类算法NaN-BP,能够在无需设置邻域参数和聚类数量的情况下得到令人满意的聚类结果.算法核心思想是首先根据数据集的分布特征,自适应迭代至对数稳定状态并获取邻域信息,并根据该邻域信息进行边界点的标记与剥离,最终以核心点为数据簇中心进行聚类.在不同规模不同分布的数据集上进行了广泛的对比实验,实验结果表明了NaN-BP的自适应性和有效性,取得了令人满意的实验结果.

A self-adaptive clustering algorithm without neighborhood parameter k and cluster number c

Traditional clustering methods often cannot avoid the selection of neighborhood parameters and the number of clusters. The optimal selection of these parameters in different shapes of data is hard to choose, and this choice is depending on prior knowledge. Aiming at the above parameter selection problem, this paper proposes a natural neighbors based border peeling clustering algorithm (NaN-BP), which can obtain satisfactory clustering results without setting the neighborhood parameters and the number of clusters. The core idea of the algorithm is to adaptively iterate to a logarithmic stable state and obtain neighborhood information according to the distribution characteristics of the data set, then mark and strip the boundary points according to the neighborhood information, and finally gather the core points as the center of the data cluster. Extensive comparative experiments is conducted on data sets of different scales and distributions, and satisfactory experimental results verify the adaptability and effectiveness of the algorithm.