Steerable Clustering for Visual Analysis of Ecosystems

International Workshop on Visual Analytics

Volumn , Issue , 2011, Pages 49-52

  Ahmed, Zafar ^a   Yost, Patrick ^a   McGovern, Amy ^a   Weaver, Chris ^a  

^a UNIVERSITY OF OKLAHOMA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85088108223     PISSN: None     EISSN: 26644487     Source Type: Conference Proceeding    
DOI: 10.2312/PE/EuroVAST/EuroVA11/049-052     Document Type: Conference Paper

References (13)

1. [AA09] ANDRIENKO G., ANDRIENKO N.: Interactive cluster analysis of diverse types of spatiotemporal data. SIGKDD Explorations Newsletter 11, 2 (2009), 19-28.
2. [ABK98] ANKERST M., BERCHTOLD S., KEIM D. A.: Similarity clustering of dimensions for an enhanced visualization of multidimensional data. In Proceedings of the IEEE Symposium on Information Visualization (InfoVis) (Research Triangle Park, NC, 1998), IEEE Computer Society, p. 52.
3. [BFG_01] BALDOCCHI D., FALGE E., GU L., OLSON R., HOLLINGER D., RUNNING S., ANTHONI P., BERNHOFER C., DAVIS K., EVANS R., FUENTES J., GOLDSTEIN A., KATUL G., LAW B., LEE X., MALHI Y., MEYERS T., MUNGER W., OECHEL W., PAW K. T., PILEGAARD K., SCHMID H. P., VALENTINI R., VERMA S., VESALA T., WILSON K., WOFSY S.: FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society 82, 11 (2001), 2415-2434.
4. [CBP09] CHOO J., BOHN S., PARK H.: Two-stage framework for visualization of clustered high dimensional data. In Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (VAST) (Atlantic City, NJ, October 2009), IEEE, pp. 67-74.
5. [CL06] CHEN K., LIU L.: IVIBRATE: Interactive visualizationbased framework for clustering large datasets. ACM Transactions on Information Systems 24, 2 (April 2006), 245-294.
6. [GCML06] GUO D., CHEN J., MACEACHREN A. M., LIAO K.: A visualization system for space-time and multivariate patterns (VIS-STAMP). IEEE Transactions on Visualization and Computer Graphics 12, 6 (November-December 2006), 1461-1474.
7. [JDN_08] JEONG D. H., DARVISH A., NAJARIAN K., YANG J., RIBARSKY W.: Interactive visual analysis of time-series microarray data. The Visual Computer 24, 12 (2008), 1053-1066.
8. [KMSZ06] KEIM D. A., MANSMANN F., SCHNEIDEWIND J., ZIEGLER H.: Challenges in visual data analysis. In Proceedings of the International Conference on Information Visualization (IV) (July 2006), IEEE Computer Society, pp. 9-16.
9. [NHM_07] NAM E. J., HAN Y., MUELLER K., ZELENYUK A., IMRE D.: Clustersculptor: A visual analytics tool for highdimensional data. In Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (VAST) (Sacramento, CA, October 30-November 1 2007), IEEE, pp. 75-82.
10. [PM99] PELLEG D., MOORE A.: Accelerating exact k-means algorithms with geometric reasoning. In Proceedings of the International Conference on Knowledge Discovery and Data Mining (SIGKDD) (1999), ACM, pp. 277-281.
11. [Wea04] WEAVER C.: Building highly-coordinated visualizations in Improvise. In Proceedings of the IEEE Symposium on Information Visualization (InfoVis) (Austin, TX, 2004), pp. 159-166.
12. [XW05] XU R., WUNSCH D. I.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16, 3 (May 2005), 645-678.
13. [ZWL08] ZHOU X., WENG E., LUO Y.: Modeling patterns of nonlinearity in ecosystem responses to temperature, CO2, and precipitation changes. Ecological Applications 18, 2 (2008), 453-466.