面向非线性特征的三维CAD模型聚类

Nonlinear Feature Oriented 3D CAD Model Clustering

拓扑结构是三维CAD模型的关键属性,其对应的描述符为图、树等非线性结构.针对现有聚类算法无法有效对这些非线性描述符聚类的问题,提出一种面向非线性特征的三维CAD模型聚类算法.首先将各类非线性特征统一表征为属性图,定义属性图序列的距离矩阵;然后以距离矩阵为输入,利用非线性凝聚层次聚类算法实现属性图的聚类;最后以聚类结果为学习样本,引入增量模型的动态归类方法归类新增模式,实现三维CAD模型可重用区域的有效聚类.理论分析及实验结果表明了该算法的有效性.

Topology structures are critical for 3D CAD models, which are described in nonlinear features such as graphs or trees. However, the existing clustering algorithms cannot cluster these kinds of nonlinear features effectively. Aimed at this situation, this paper proposes a nonlinear feature oriented 3D CAD model clustering algorithm. Firstly, various nonlinear features are characterized as attribute graphs uniformly, and the distance matrix of attribute graphs sequence is defined; secondly, with the distance matrix as input, a nonlinear agglomerative hierarchical clustering algorithm is put forward to cluster the attribute graphs; finally, using the clustering results as learning samples, a dynamic classification algorithm is introduced to classify the new added graphs. The reusable regions of 3D CAD models are clustered effectively based on the above algorithm. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed algorithm.