HOV3: An approach to visual cluster analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4093 LNAI, Issue , 2006, Pages 316-327

  Zhang, Ke Bing ^a   Orgun, Mehmet A ^a   Zhang, Kang ^b  

^a MACQUARIE UNIVERSITY   (Australia)

^b The University of Texas at Dallas   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; KNOWLEDGE ACQUISITION; OPTIMIZATION; ALGORITHMS; CLUSTER ANALYSIS; DATA VISUALIZATION; ITERATIVE METHODS; STOCHASTIC SYSTEMS; VISUALIZATION;

CLUSTER INFORMATION; CLUSTERING; CLUSTERING ALGORITHMS; HYPOTHESIS ORIENTED VERIFICATION; CLUSTERING PROCESS; DATA DISTRIBUTION; HIGH DIMENSIONAL DATA; HIGH-DIMENSIONAL DATA ANALYSIS; VERIFICATION-AND-VALIDATION; VISUAL CLUSTER ANALYSIS; VISUALIZATION METHOD; VISUALIZATION TECHNIQUE;

DATA REDUCTION; CLUSTERING ALGORITHMS;

EID: 33749392363     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11811305_35     Document Type: Conference Paper

References (25)

1. Alpern B. and Carter L.: Hyperbox. Proc. Visualization '91, San Diego, CA (1991) 133-139
2. Ankerst M., Breunig MM., Kriegel, Sander HP. J.: OPTICS: Ordering points to identify the clustering structure. Proc. of ACM SIGMOD Conference (1999) 49-60
3. Ankerst M., and Keim D.: Visual Data Mining and Exploration of Large Databases. 5th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'01), Freiburg, Germany, September (2001)
4. Berkhin P.: Survey of clustering data mining techniques. Technical report, Accrue Software (2002)
5. Cook D.R., Buja A., Cabrea J., and Hurley H.: Grand tour and projection pursuit. Journal of Computational and Graphical Statistics Volume: 23 (1995) 225-250
6. Chen K. and Liu L.: VISTA: Validating and Refining Clusters via Visualization. Journal of Information Visualization Volume: 13 (4) (2004) 257-270
7. Chernoff H.: The Use of Faces to Represent Points in k-Dimensional Space Graphically. Journal Amer. Statistical Association, Volume: 68 (1973) 361-368
8. Cleveland W.S.: Visualizing Data. AT&T Bell Laboratories, Murray Hill, NJ, Hobart Press, Summit NJ. (1993)
9. Ester M., Kriegel HP., Sander J., Xu X.: A density-based algorithm for discovering clusters in large spatial databases with noise. 2nd International Conference on Knowledge Discovery and Data Mining (1996)
10. Fienberg S. E.: Graphical methods in statistics. American Statisticians Volume: 33 (1979) 165-178
11. Guha S., Rastogi R., Shim K.: CURE: An efficient clustering algorithm for large databases. In Proc. of ACM SIGMOD Int'l Conf. on Management of Data, ACM Press (1998) 73-84
12. Hinneburg, A. Keim D. A., Wawryniuk M.: HD-Eye-Visual Clustering of High dimensional Data. Proc. of the 19th International Conference on Data Engineering, (2003) 753-755
13. Hoffman P. E. and Grinstein G.: A survey of visualizations for high-dimensional data mining. In Fayyad U., Grinstein G. G. and Wierse A. (eds.) Information visualization in data mining and knowledge discovery, Morgan Kaufmann Publishers Inc. (2002) 47-82
14. Inselberg A.: Multidimensional Detective. Proc. of IEEE Information Visualization '97 (1997) 100-107
15. Jain A., Murty M. N., and Flynn P.J.: Data Clustering: A Review. ACM Computing Surveys Volume: 31(3) (1999) 264-323
16. Kandogan E.: Visualizing multi-dimensional clusters, trends, and outliers using star coordinates. Proc. of ACM SIGKDD Conference, (2001) 107-116
17. Keim D.A. And Kriegel HP.: VisDB: Database Exploration using Multidimensional Visualization. Computer Graphics & Applications (1994) 40-49
18. Maria Cristina Ferreira de Oliveira, Haim Levkowitz: From Visual Data Exploration to Visual Data Mining: A Survey. IEEE Transaction on Visualization and Computer Graphs Volume: 9(3) (2003) 378-394
19. Pampalk E., Goebl W., and Widmer G.: Visualizing Changes in the Structure of Data for Exploratory Feature Selection. SIGKDD '03, Washington, DC, USA (2003)
20. Pickett R. M.: Visual Analyses of Texture in the Detection and Recognition of Objects. Picture Processing and Psycho-Pictorics, Lipkin B. S., Rosenfeld A. (eds.) Academic Press, New York, (1970) 289-308
21. Qian Y., Zhang G., and Zhang K.: FAÇADE: A Fast and Effective Approach to the Discovery of Dense Clusters in Noisy Spatial Data. In Proc. ACM SIGMOD 2004 Conference, ACM Press (2004) 921-922
22. Ribarsky W., Katz J., Jiang F. and Holland A.: Discovery visualization using fast clustering. Computer Graphics and Applications, IEEE, Volume: 19 (1999) 32-39
23. Sheikholeslami G., Chatterjee S., Zhang A.: WaveCluster: A multi-resolution clustering approach for very large spatial databases. Proc. of 24th Intl. Conf. On Very Large Data Bases (1998) 428-439.
24. Shneiderman B.: Inventing Discovery Tools: Combining Information Visualization with Data Mining. Discovery Science 2001, Proceedings. Lecture Notes in Computer Science Volume: 2226 (2001) 17-28
25. Zhang T., Ramakrishnan R. and Livny M.: BIRCH: An efficient data clustering method for very large databases. In Proc. of SIGMOD96, Montreal, Canada (1996) 103-114