Contextual Hausdorff dissimilarity for multi-instance clustering

Proceedings - 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012

Volumn , Issue , 2012, Pages 870-873

  Chen, Ying ^a   Wu, Ou ^b  

^a BEIJING ELECTRONIC SCIENCE AND TECHNOLOGY INSTITUTE   (China)

^b INSTITUTE OF AUTOMATION   (China)

Author keywords

Clustering; Hausdorff dissimilarity; Instance

Indexed keywords

CLUSTERING; CLUSTERING PROBLEMS; HAUSDORFF; INSTANCE; REAL WORLD DATA; SYNTHETIC DATA;

FUZZY SYSTEMS; VIRTUAL REALITY;

CLUSTERING ALGORITHMS;

EID: 84872907951     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FSKD.2012.6233889     Document Type: Conference Paper

References (7)

1. H.P. Kriegel, A. Pryakhin, M. Schubert, An EM-approach for clustering multi-instance objects. Lecture Notes in Computer Science, vol. 3918, Page(s): 139-148, 2006.
2. M.L. Zhang, Z.H. Zhou, Multi-Instance clustering with applications to multi-Instance Prediction. Applied Intelligence, vol. 31, No. 1, Page(s): 47-68, 2009.
3. D.L. Zhao, Z.C. Lin, X.O. Tang, Contextual distance for data perception. IEEE International Conference on Computer Vision, Page(s): 1-8, 2007.
4. H. Jegou, H. Harzallah, C. Schmid, A contextual dissimilarity measure for accurate and efficient image search. IEEE International Conference on Computer Vision and Pattern Recognition, Page(s): 1-8, 2007.
5. J. Wang, J. Zucker, Solving the mMultiple-instance problem: a lazy learning approach. IEEE International Conference on Machine Learning, Page(s): 1119-1125, 2000.
6. J.S. Bruner, A.L. Minturn, Perceptual identification and perceptual organization. Journal of General Psychology, vol. 53, Page(s): 21-28, 1955.
7. http://archive.ics.uci.edu/ml/datasets.html.