SS-ClusterTree: A subspace clustering based indexing algorithm over high-dimensional image features

CIVR 2008 - Proceedings of the International Conference on Content-based Image and Video Retrieval

Volumn , Issue , 2008, Pages 95-104

  Xu, Hongli ^a   Yu, Dantong ^b   Xu, De ^a   Zhang, Aidong ^c  

^a BEIJING JIAOTONG UNIVERSITY   (China)

^b BROOKHAVEN NATIONAL LABORATORY   (United States)

^c State University of New York   (United States)

Author keywords

Cluster Tree; Index; Retrieval; Subspace cluster

Indexed keywords

ATTRACTION FORCES; CLUSTER STRUCTURES; CLUSTER TREE; CLUSTERING APPROACHES; CLUSTERING DATUM; DATA ITEMS; DATA POINTS; DATA SETS; DOMAIN KNOWLEDGE; HIGH DIMENSIONS; HIGH-DIMENSIONAL IMAGES; IMAGE DATABASE; IMAGE INFORMATIONS; INDEX; INDEX STRUCTURES; INDEXING ALGORITHMS; INTERNAL STRUCTURES; PRIOR KNOWLEDGE; RAPID GROWTHS; RETRIEVAL; SUBSPACE CLUSTER; SUBSPACE CLUSTERING; VIDEO DATUM;

CLUSTERING ALGORITHMS; IMAGE RETRIEVAL; INDEXING (OF INFORMATION); INFORMATION SERVICES; QUERY PROCESSING; VIDEO RECORDING;

KNOWLEDGE BASED SYSTEMS;

EID: 57549106262     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1386352.1386369     Document Type: Conference Paper

References (19)

1. Dantong Yu and Aidong Zhang. ClusterTree: integration of cluster representation and nearest-neighbor search for large data sets with high dimensions[J].IEEE Transactions on Knowledge and Data Engineering. 2003, 15(5):1316-1337.
2. Ester M, Kriegel H, Sander J, Xu XW. A density-based algorithm for discovering clusters in large spatial databases with noise. Proc. of the 2nd Int'l Conf. on Knowledge Discovery and Data Mining (KDD'96). Portland: AAA1 Press, 1996. 226-231.
3. Hinneburg A, Keim D. An efficient approach to clustering in large multimedia databases with noise. Proc. of the 4th Int'l Conf. on Knowledge Discovery and Data Mining (KDD'98). New York: AAAI Press, 1998. 58-65.
4. Wang W, Yang J, Muntz RR. STING: A statistical information grid approach to spatial data mining. Proc. of the 23rd Int'l Conf. on Very Large Data Bases. Athens: Morgan Kaufmann, 1997. 186-195.
5. Sheikholeslami G, Chatterjee S, Zhang AD. WaveCluster: A multi-resolution clustering approach for very large spatial databases. Proc. of the 24th Int'l Conf. on Very Large Data Bases. New York: Morgan Kaufmann, 1998. 428-439.
6. Hinneburg, A., Keim, D.A.: An optimal grid-clustering: Towards breaking the curse of dimensionality in highdimensional clustering. The VLDB Journal. 1999. 506-517
7. Rakesh A, Johanners G, Dimitrios G, Prabhakar R. Automatic subspace clustering of high dimensional data for data mining applications. Proc. of the 1998 ACM SIGMOD Int'l Conf. on Management of Data. Minneapolis: ACM Press, 1998. 94-105
8. Karin Kailing, Hans-Peter Kriegel, Peer Kroger. Density- Connected Subspace Clustering for High-Dimensional Data. In Proc. 4th SIAM Int. Conf. on Data Mining, pp.246-257, Lake Buena Vista,FL,2004
9. Carlotta Domeniconi, D. Papadopoulos, D. Gunopulos, Sheng Ma. Subspace Clustering of High Dimensional Data, SIAM International Conference on Data Mining (SDM), Apr. 2004, http://www.cs.ucr.edu/~dimitris/publications.html
10. Hongli Xu, De Xu, and Enai Lin. An applicable hierarchical clustering algorithm for content based image retrieval, In Proc. Computer Vision/Computer Graphics Collaboration Techniques and Applications, MIRAGE'07 International Conference, France, Mar. 2007 pp.82-92
11. W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, C. Faloutsos, and G. Taubin, "The QBIC Project: Querying Images By Content Using Color, Texture, and Shape," Proc. of the SPIE Conf. on Storage and Retrieval for Image and Video Databases II, 1993. 173-187.
12. Tian Zhang, Raghu Ramakrishnan, and Miron Livny. BIRCH: An Efficient Data Clustering Method for Very Large Databases. In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pages 103-114, Montreal, Canada, 1996.
13. Guha S, Rastogi R, Shim K. CURE: an efficient clustering algorithm for large databases. Proc. of the ACM SIGMOD Int'l Conf. on Management of Data. Seattle: ACM Press, 1998.73-84
14. Lance Parsons, Ehtesham Haque, Huan Liu. Subspace Clustering for High Dimensional Data: A Review SIGKDD Explorations, Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining, 6(l):90, 2004.
15. C.H Cheng, A.W.Fu,Y.Zhang. Entropy-based subspace cluster for mining numerical data. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining,pages 84-93. 1999
16. Sanjay Goil, H. Nagesh, A. Choudhary. MAFIA: Efficient and Scalable Subspace Clustering Clustering for Very Large Data Sets. Technical Report CPDC-TR-9906-010, Northwestern University, 2145 Sheridan Road, Evanston IL 60208, June 1999
17. J.W Chang, D.S.Jin. A new cell-based clustering method for large, high-dimensional data in data mining applications. In Proceedings of the 2002 ACM symposium on Applied computing, pages 503-507. 2002
18. Kyoung-Gu Woo, Jeong-Hoon Lee, Myoung-Ho Kim, Yoon- Joon Lee. FINDIT: a fast and intelligent subspace clustering algorithm using dimension voting. Information & Software Technology, VOL 46(4), 2004, pp 255-271.
19. WANG Jian-hui, SHEN Zhan, HU Yun-fa. An Applicable and Efficient Clustering Algorithm[J]. Journal of Software,2004,15(5):697-705.