In pattern recognition, patterns are described in terms of features. The features form feature vectors in the feature space. In the light of the phenomenon of gravitation in star clusters, we define patterns in the feature space to self-organize into clustering networks called “vector gravitation clustering networks” in this study. In the proposed clustering method, one called “vector gravitational force” is employed for the similarity measure in the feature space. Then by means of the “vector gravitational force”, patterns self-organize clustering networks called “vector gravitation clustering networks” in the feature space. The proposed clustering method is applied to experiments. The experimental results show workability of the proposed clustering method. It is revealed that patterns tend to have more called “vector gravitational force” between ones of the same categories than between ones of the different categories in the feature space. Finally, further performance analysis employing the ANOVA (“analysis of variance”) and the Newman-Keul procedure indicates potentiality of the proposed clustering method. As being inspired by the phenomenon of gravitation in star clusters and by using the “vector gravitational force” for similarity measure, “interpretability” is one obvious advantage of the proposed clustering method, and it may be viewed as one natural clustering method.

在模式识别中，模式是根据特征来描述的。特征在特征空间中形成特征向量。根据星团中的引力现象，我们在特征空间中定义模式，以自组织成名为“矢量引力聚类网络”的聚类网络。在提出的聚类方法中，特征空间中的相似性度量采用了一种称为“矢量重力”的方法。然后借助“矢量引力”，在特征空间中将图案自组织成聚类网络，称为“矢量引力聚类网络”。提出的聚类方法应用于实验。实验结果表明了该聚类方法的可行性。结果表明，在特征空间中，与相同类别之间的模式相比，与不同类别中的模式之间的模式倾向于具有更多的“矢量引力”。最后，使用ANOVA（“方差分析”）和Newman-Keul程序进行的进一步性能分析表明了所提出的聚类方法的潜力。由于受到星团中的引力现象的启发并且通过使用“矢量引力”进行相似性度量，“可解释性”是所提出的聚类方法的明显优势之一，并且可以被视为一种自然聚类方法。使用ANOVA（“方差分析”）和Newman-Keul程序进行的进一步性能分析表明了所提出的聚类方法的潜力。由于受到星团中的引力现象的启发并且通过使用“矢量引力”进行相似性度量，“可解释性”是所提出的聚类方法的明显优势之一，并且可以被视为一种自然聚类方法。使用ANOVA（“方差分析”）和Newman-Keul程序进行的进一步性能分析表明了所提出的聚类方法的潜力。由于受到星团中的引力现象的启发并且通过使用“矢量引力”进行相似性度量，“可解释性”是所提出的聚类方法的明显优势之一，并且可以被视为一种自然聚类方法。