Graph-based heuristics for recognition of machined features from a 3D solid model
Computer-Aided Design
Shape feature determination usiang the curvature region representation
SMA '97 Proceedings of the fourth ACM symposium on Solid modeling and applications
Topology matching for fully automatic similarity estimation of 3D shapes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
Skeleton Based Shape Matching and Retrieval
SMI '03 Proceedings of the Shape Modeling International 2003
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Machining Feature-Based Comparisons of Mechanical Parts
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
Content-based assembly search: A step towards assembly reuse
Computer-Aided Design
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
Automatic comparison and remeshing applied to CAD model modification
Computer-Aided Design
Relaxed lightweight assembly retrieval using vector space model
Computer-Aided Design
Mining and indexing graphs for supergraph search
Proceedings of the VLDB Endowment
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This paper presents an approach for extracting common design structures from a set of B-rep models. Here, a B-rep model is first transformed into a face adjacency graph (FAG), and then each node of an FAG is mapped to a point in a two-dimensional plane after representing face shape characteristics with two coordinates. Thus, the common design structures are just the frequently appearing subgraphs of FAGs drawn in a plane. In the area of data mining, the apriori-based graph mining (AGM) is a well-known algorithm for solving the problem of frequent subgraph discovery, but its efficiency is still low in processing large graphs like the FAGs of CAD models. In this research, we develop a novel algorithm that improves AGM in two aspects. First, the exact subgraph-isomorphism checking is replaced by comparing the shape descriptors composed from the point coordinates corresponding to the nodes of the subgraphs in question. Second, a new approach for generating frequent subgraph candidates is adopted, which allows large frequent subgraphs to be found in fewer iterations. Experiments show that the proposed method is efficient and can produce a reasonable result.