Clustering by Using the Way of Atomic Fission

Cluster analysis, which focuses on the grouping and categorization of similar elements, is widely used in various fields of research. Inspired by the phenomenon of atomic fission, this paper proposes a novel density-based clustering algorithm, called fission clustering (FC). It focuses on mining the dense families of clusters in the dataset and utilizes the information of the distance matrix to fissure the dataset into subsets. A K-nearest neighbor (KNN) local density indicator is applied to identify and remove the points of sparse areas so as to obtain a dense subset that consists of the dense families of clusters. The algorithm, denoted as FC-KNN, is achieved by merging FC and KNN local density indicator. Several frequently-used datasets were applied to test the performance of the proposed clustering approach and to compare the results with those of other algorithms. The comprehensive comparisons indicate that the proposed method has advantages over other common methods.聚类分析旨在对相似元素进行分组与归类，现已广泛应用于各类研究领域。受原子裂变现象启发，本文提出一种新型基于密度的聚类算法，命名为裂变聚类（fission clustering, FC）。该算法旨在挖掘数据集中的聚类稠密簇群，并借助距离矩阵信息将数据集拆分为若干子集。本文采用K近邻（K-nearest neighbor, KNN）局部密度指标，用于识别并剔除稀疏区域的样本点，以得到由聚类稠密簇群构成的稠密子集。将FC与KNN局部密度指标相结合，得到本文所提的FC-KNN算法。本文选用多款常用数据集对所提聚类方法的性能进行测试，并将其与其他算法的实验结果进行对比。综合对比结果表明，本文所提方法相较于其他常见聚类算法具有更优的性能。