Extracting Understandable 3D Object Groups with Multiple Similarity Metrics

  • Antonio Adán
  • Miguel Adán
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)


Some of the main difficulties involved in the clustering problem are the interpretation of the clusters and the choice of the number of clusters. The imposition of a complete clustering, in which all the objects must be classified might lead to incoherent and not convincing groups. In this paper we present an approach which alleviates this problem by proposing incomplete but reliable clustering strategies. The method is based on two pillars: using a set of different metrics which are evaluated through a clustering confidence measure and achieving a hard/soft clustering consensus. This method is particularly addressed to 3D shape grouping in which the objects are represented through geometric features defined over mesh models. Our approach has been tested using eight metrics defined on geometrical descriptors in a collection of freeshape objects. The results show that in all cases the algorithm yields coherent and meaningful groups for several numbers of clusters. The clustering strategy here proposed might be useful for future developments in the unsupervised grouping field.


3D shape representation 3D Shape similarity 3D Object Clustering 


  1. 1.
    Tan, P., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison Wesley (2005)Google Scholar
  2. 2.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)CrossRefGoogle Scholar
  3. 3.
    Ertoz, L., Steinbach, M., Kumar, V.: Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data. In: Proceedings of SDM 2003, SIAM Int’l Conf on Data Mining, San Francisco, CA (2003)Google Scholar
  4. 4.
    Basu, S., Bilenko, M., Mooney, R.: A probabilistic framework for semi-supervised clustering. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seatle, WA (2004)Google Scholar
  5. 5.
    Davidson, I., Ravi, S.: Clustering with constraints: feasibility issues and the k-means algorithm. In: Proceedings of SDM 2006: SIAM International Conference on Data Mining, Newport Beach, CA (2005)Google Scholar
  6. 6.
    Cheng, Z., Zhou, D., Wang, C., Guo, J., Wang, W., Ding, B., Shi, B.-L.: CLINCH: Clustering Incomplete High-Dimensional Data for Data Mining Application. In: Zhang, Y., Tanaka, K., Yu, J.X., Wang, S., Li, M. (eds.) APWeb 2005. LNCS, vol. 3399, pp. 88–99. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Hathaway, R.J., Bezdek, J.C.: Fuzzy c-means clustering of incomplete data. IEEE Trans. System Man and Cybernetic Part B 31(5), 735–744 (2001)CrossRefGoogle Scholar
  8. 8.
    Zhang, D.-Q., Chen, S.-C.: Clustering incomplete data using kernel-based fuzzy c-means Algorithm. Neural Processing Letters 18(3), 155–162 (2003)CrossRefGoogle Scholar
  9. 9.
    Himmelspach, L., Conrad, S.: Fuzzy clustering of incomplete data based on cluster dispersion. In: 13th International Conference on Information Processing and Management of UncertaintyGoogle Scholar
  10. 10.
    Jayanti, S., Kalyanaraman, Y., Ramani, K.: Shape-based clustering for 3D CAD objects: A comparative study of effectiveness. In: Computer-Aided Design, vol. 41(12), pp. 999–1007 (2009)Google Scholar
  11. 11.
    Chakraborty, T.: Shape-based Clustering of Enterprise CAD Databases. Computer-Aided Design & Applications 2(1-4), 145–154 (2005)Google Scholar
  12. 12.
    Ward, J.H.: Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association 58(301), 236–244 (1963)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Adán, A., Adán, M.: Incomplete-Clustering Consensus Strategy Using RC-images. Pattern Recognition. 3DVC&R, UCLM Technical Report (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Antonio Adán
    • 1
  • Miguel Adán
    • 1
  1. 1.Departamento Ingeniería E. E. A. C.Universidad de Castilla La ManchaSpain

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