ClusterMap: Labeling clusters in large datasets via visualization

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2004, Pages 285-293

  Chen, Keke ^a   Liu, Ling ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

Cluster Labeling; Cluster Visualization; Data Clustering; Human Factors in Clustering

Indexed keywords

CLUSTER LABELING; CLUSTER VISUALIZATION; DATA CLUSTERING; HUMAN FACTOR IN CLUSTERING;

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; DATA REDUCTION; PROBLEM SOLVING; QUALITY ASSURANCE;

DATA ACQUISITION;

EID: 18744394353     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. Chen, K. and Liu, L. Cluster Rendering of Skewed Datasets via Visualization. In Proceedings of ACM Symposium on Applied Computing 2003 (SAC03), Melborne, FL. 2003
2. Guha, G., Rastogi, R., and Shim, K. CURE: An efficient clustering algorithm for large databases. In Proceedings of the 1998 ACMSIGMOD, Seattle, WA. 1998
3. Keim, D. Visual Exploration of Large Data Sets. Communications of the ACM, August 2001, V. 44. No. 8
4. Cook, D.R, Buja, A., Cabrea, J. and Hurley, H. Grand tour and projection pursuit. Journal of Computational and Graphical Statistics, 4(3) 155-172, 1995
5. Yang, L. Interactive Exploration of Very Large Relational Datasets through 3D Dynamic Projections, in Proc. of SIGKDD2000, Boston, MA, 2000
6. Kandogan, E. Visualizing Multi-dimensional Clusters, Trends, and Outliers using Star Coordinates, in Proc. of SIGKDD2001, San Francisco, CA, 2001.
7. Jain, A. and Dubes, R. Algorithms for Clustering Data. Prentice hall, Englewood Cliffs, NJ, 1988
8. Grinstein, G., Ankerst, M. and Keim, D. Visual Data Mining: Background, Applications, and Drug Discovery Applications, Tutorial at ACM SIGKDD2002, Edmonton, Canada. 2002
9. Jain, A., Murty, M.N. and Flynn, P.J. Data Clustering: A Review. ACM Computing Surveys, 31(3), P264-323, 1999
10. Ester, M., Kriegel, H., Sander, J. and Xu, X. A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, in Proc. of VLDB96, Bombay, India, 1996
11. Hinneburg, A. and Keim, D. An Efficient Approach to Clustering in Large Multimedia Databases with Noise, in Proc. of KDD98, NYC, NY, 1998
12. Shneiderman, B. Inventing Discovery Tools: Combinning Information Visualization With Data Mining. Information Visualization, 1, p5-12, 2002
13. Halkidi, M., Batistakis, Y., and Vazirgiannis, M. Cluster Validity Methods: Part I&II, SIGMOD Record, Vol31, No.2&3, 2002
14. Sheikholeslami, G., Chatterjee, S. and Zhang, A. Wavecluster: A multi-resolution clustering approach for very large spatial databases, In Proc. VLDB98, NYC, NY, 1998
15. Ankerst, M., Breunig, M., Kriegel, H. and Sander, J. OPTICS: Ordering Points To Identify the Clustering Structure. In Proc. of SIGMOD1999, Philadelphia, PA, 1999
16. Xu, X., Ester, M., Kriegel, H. and Sander, J. A Distribution-based Clustering Algorithm for Mining in Large Spatial Databases. In Proc. of ICDE98, Orlando, FL, 1998
17. Karypis, G., Han, E. and Kumar, V. CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modelling. IEEE Computer, 32(8), 68-75, 1999
18. Gallier, J. Geometric methods and applications: for computer science and engineering. Springer-Verlag, NY, 2001
19. Zhang, T., Ramakrishnan, R. and Livny, M. BIRCH: An efficient data clustering method for very large databases, In Proc. of SIGMOD96, 103-114, Montreal, Canada, 1996
20. Chen, K. and Liu, L. A visual framework invites human into clustering process. In Proc. of Statistical and Scientific Database Management (SSDBM03), Boston, MA, 2003
21. Chen, K. and Liu, L. Validating and Refining Clusters via Visual Rendering. In Proc. of Intl. Conf. on Data Mining (ICDM03), Melborne, FL, 2003
22. Huber, P. Massive data sets workshop: The morning after Massive Data Set, National Academy Press, 1996
23. Hinneburg, A., Keim, D. and Wawryniuk, M. Visual Mining of High-dimensional data. IEEE Computer Graphics and Applications. 19(5), 1999
24. Li, C., Chang, E., Garcia-Molina, H. and Wiederhold, G. Clustering for Approximate Similarity Search in High-Dimensional Spaces, IEEE Trans. on Knowledge and Data Engineering, 14(4), 792-808, 2002
25. Freidman, J.H., Bentley, J.L., and Finkel, R.A. An algorithm for finding best matches in Logarithmic Expected Time. ACM Trans. on Mathematical Software. 3(3), 1977
26. Sonka, M., Hlavac, V. and Boyle, R. Image Processing, Analysis, and Machine Vision. Brooks/Cole Publishing, Pacific Grove, CA, 1999