Elements of information theory
Elements of information theory
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Database techniques for archival of solid models
Proceedings of the sixth ACM symposium on Solid modeling and applications
Clustering Algorithms
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
A Survey of Content Based 3D Shape Retrieval Methods
SMI '04 Proceedings of the Shape Modeling International 2004
SMI '04 Proceedings of the Shape Modeling International 2004
2D Maps for Visual Analysis and Retrieval in Large Multi-Feature 3D Model Databases
VIS '04 Proceedings of the conference on Visualization '04
Benchmarking CAD search techniques
Proceedings of the 2005 ACM symposium on Solid and physical modeling
Cluster Utility: A New Metric for Clustering Biological Sequences
CSBW '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops
A 3D object classifier for discriminating manufacturing processes
Computers and Graphics
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
On visual similarity based 2D drawing retrieval
Computer-Aided Design
A practical generative design method
Computer-Aided Design
Interaction techniques for integrated content-based enterprise search
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part III
Relaxed lightweight assembly retrieval using vector space model
Computer-Aided Design
Consensus strategy for clustering using RC-images
Pattern Recognition
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3D shape retrieval and clustering is of current interest in several different fields, including mechanical engineering. Several new shape representations for 3D objects are continuing to emerge. Most shape representations are embedded in a variety of feature spaces. However, some of the recently reported shape representations are embedded in arbitrary metric spaces, i.e. distance spaces, rather than in multi-dimensional feature space. For such representations, the only operations available on the data objects are distance calculations between the objects. In addition, some of the view-based representations are embedded in non-metric spaces where the representations and the corresponding distances do not follow the triangle inequality. For shape clustering applications, most existing algorithms assume the shape representations either to be embedded in a multi-dimensional feature space or a metric distance space, making it difficult to evaluate several shape representations that do not conform to these assumptions. Therefore, two different approaches were evaluated for using the distance features of a shape to obtain clustering results. In the first method, the original distances are transformed into feature space using a multi-dimensional scaling approach for use with K-means clustering. The second approach directly uses the original distances with a distance-based clustering algorithm. We compared the clustering effectiveness of these two approaches using a classified benchmark database of 3D models. The effect of using different shape descriptors and number of clusters was studied using four measures of clustering effectiveness. Several statistical methods, including the Rand Index and Mutual Information Index, were used to objectively evaluate the clustering efficacy.