论文标题
通过一维方法进行广义聚类
Toward Generalized Clustering through an One-Dimensional Approach
论文作者
论文摘要
在将簇的概念推广到通过一些相对狭窄的桥梁链接到其他簇的簇之后，基于一个基于一个集聚的聚类，更具体地是单个链接，用于从相应的特征空间中获得的单维切片。相对于点的无聚类均匀分布和正态分布的分析，以及一个一维聚类模型，以两个间隔为特征，其特征在于两个间隔的高密度，而点高密度，则该模型的分析是相对于较高的点，从而说明了该方法的潜力。然后将这种部分聚类方法视为特征选择和群集识别的手段，并在某些假设情况下描述并说明了两种简单但潜在的各自方法。
After generalizing the concept of clusters to incorporate clusters that are linked to other clusters through some relatively narrow bridges, an approach for detecting patches of separation between these clusters is developed based on an agglomerative clustering, more specifically the single-linkage, applied to one-dimensional slices obtained from respective feature spaces. The potential of this method is illustrated with respect to the analyses of clusterless uniform and normal distributions of points, as well as a one-dimensional clustering model characterized by two intervals with high density of points separated by a less dense interstice. This partial clustering method is then considered as a means of feature selection and cluster identification, and two simple but potentially effective respective methods are described and illustrated with respect to some hypothetical situations.
