A prediction-based visual approach for cluster exploration and cluster validation by HOV

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

Volumn 4702 LNAI, Issue , 2007, Pages 336-349

  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

Cluster analysis; Predictive knowledge discovery; Visualization

Indexed keywords

CLUSTER ANALYSIS; DATA VISUALIZATION; KNOWLEDGE ACQUISITION; USER INTERFACES;

GROUPED DATA DISTRIBUTIONS; HIGH DIMENSIONAL DATA ANALYSIS; PREDICTIVE KNOWLEDGE DISCOVERY;

DATA MINING;

EID: 38049113247     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74976-9_32     Document Type: Conference Paper

References (23)

1. Ankerst, M., Breunig, M.M., Kriegel, S.H.P.J.: OPTICS: Ordering points to identify the clustering structure. In: Proc. of ACM SIGMOD Conference, pp. 49-60. ACM Press, New York (1999)
2. Ankerst, M., Keim, D.: Visual Data Mining and Exploration of Large Databases. In: 5th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'01), Freiburg, Germany (September 2001)
3. Berkhin, P.: A Survey of Clustering Data Mining Techniques. In: Jacob, K., Charles, N., Marc, T. (eds.) Grouping Multidimensional Data, pp. 25-72. Springer, Heidelberg (2006)
4. Chen, K., Liu, L.: iVIBRATE: Interactive visualization-based framework for clustering large datasets. ACM Transactions on Information Systems (TOIS) 24(2), 245-294 (2006)
5. Faloutsos, C., Lin, K.: Fastmap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia data sets. In: Proc. of ACM-SIGMOD, pp. 163-174 (1995)
6. Fleming, W.: Functions of Several Variables. In: Gehring, F.W., Halmos, P.R. (eds.) 2nd edn. Springer, Heidelberg (1977)
7. Huang, Z., Cheung, D.W., Ng, M.K.: An Empirical Study on the Visual Cluster Validation Method with Fastmap. In: Proc. of DASFAA01, pp. 84-91 (2001)
8. Huang, Z., Lin, T.: A visual method of cluster validation with Fastmap. In: Terano, T., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 153-164. Springer, Heidelberg (2000)
9. Jain, A., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31(3), 264-323 (1999)
10. Jolliffe Ian, T.: Principal Component Analysis. Springer Press, Heidelberg (2002)
11. Kandogan, E.: Visualizing multi-dimensional clusters, trends, and outliers using star coordinates. In: Proc. of ACM SIGKDD Conference, pp. 107-116. ACM Press, New York (2001)
12. Kohonen, T.: Self-Organizing Maps, 2nd extended edn. Springer, Berlin (1997)
13. Kaski, S., Sinkkonen, J., Peltonen, J.: Data Visualization and Analysis with Self-Organizing Maps in Learning Metrics. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, pp. 162-173. Springer, Heidelberg (2001)
14. Kruskal, J.B., Wish, M.: Multidimensional Scaling, SAGE university paper series on quantitive applications in the social sciences, pp. 7-11. Sage Publications, CA (1978)
15. Oliveira, M.C., Levkowitz, H.: From Visual Data Exploration to Visual Data Mining: A Survey. IEEE Transaction on Visualization and Computer Graphs 9(3), 378-394 (2003)
16. Pampalk, E., Goebl, W., Widmer, G.: Visualizing Changes in the Structure of Data for Exploratory Feature Selection. In: SIGKDD '03, Washington, DC, USA (2003)
17. Sprenger, T.C., Brunella, R., Gross, M.H.: H-BLOB: A Hierarchical Visual Clustering Method Using Implicit Surfaces. In: Proc. of the conference on Visualization '00, pp. 61-68. IEEE Computer Society Press, Los Alamitos (2000)
18. Seo, J., Shneiderman, B.: From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments. In: Hemmje, M., Niederée, C., Risse, T. (eds.) From Integrated Publication and Information Systems to Information and Knowledge Environments. LNCS, vol. 3379, Springer, Heidelberg (2005)
19. Shneiderman, B.: Inventing Discovery Tools: Combining Information Visualization with Data Mining. In: Jantke, K.P., Shinohara, A. (eds.) DS 2001. LNCS (LNAI), vol. 2226, pp. 17-28. Springer, Heidelberg (2001)
20. Weiss, S.M., Indurkhya, N.: Predictive Data Mining: A Practical Guide. Morgan Kaufmann Publishers, San Francisco (1998)
21. Vilalta, R., Stepinski, T., Achari, M.: An Efficient Approach to External Cluster Assessment with an Application to Martian Topography, Technical Report, No. UH-CS-05-08, Department of Computer Science, University of Houston (2005)
22. 3, An Approach for Cluster Analysis. In: Li, X., Zaïane, O.R., Li, Z. (eds.) ADMA 2006. LNCS (LNAI), vol.4093, pp. 317-328. Springer, Heidelberg (2006)
23. Zhang, K-B., Orgun, M.A., Zhang, K.: A Visual Approach for External Cluster Validation. In: Proc. of IEEE Symposium on Computational Intelligence and Data Mining (CIDM2007), Honolulu, Hawaii, USA, April 1-5, 2007, pp. 576-582. IEEE Press, Los Alamitos (2007)