Parallel k-means clustering for quantitative ecoregion delineation using large data sets

Procedia Computer Science

Volumn 4, Issue , 2011, Pages 1602-1611

  Kumar, Jitendra ^a   Mills, Richard T ^a   Hoffman, Forrest M ^a   Hargrove, William W ^b  

^a OAK RIDGE NATIONAL LABORATORY   (United States)

^b USDA FOREST SERVICE   (United States)

Author keywords

Data mining; Ecoregionalization; High performance computing; K means clustering

Indexed keywords

COMPUTATIONAL PROBLEM; ECOLOGICAL PROBLEM; ECOREGIONALIZATION; ECOREGIONS; ENVIRONMENTAL CONDITIONS; ENVIRONMENTAL MONITORING; ENVIRONMENTAL SCIENCE; ENVIRONMENTAL SCIENTISTS; GROUND-BASED REMOTE SENSING; HABITAT PRESERVATION; HIGH PERFORMANCE COMPUTING; HIGH-RESOLUTION DATASETS; K-MEANS CLUSTERING; KNOWLEDGE EXTRACTION; LARGE DATASETS; LONG TIME SERIES; MODEL OUTPUTS; MODEL-MEASUREMENT INTERCOMPARISON; SATELLITE REMOTE SENSING; SPATIO-TEMPORAL CLUSTERING; SPECIES RANGES; TEMPORAL RESOLUTION;

CLIMATE MODELS; CLUSTERING ALGORITHMS; COMPUTER SOFTWARE SELECTION AND EVALUATION; ECOLOGY; REMOTE SENSING; SUPERCOMPUTERS; TIME SERIES;

CLUSTER ANALYSIS;

EID: 79958256828     PISSN: None     EISSN: 18770509     Source Type: Conference Proceeding    
DOI: 10.1016/j.procs.2011.04.173     Document Type: Conference Paper

References (14)

1. W. W. Hargrove, F. M. Hoffman, Using multivariate clustering to characterize ecoregion borders, Computing in Science & Engineering 1(4) (1999) 18-25. (Pubitemid 129604435)
2. W. W. Hargrove, J. P. Spruce, G. E. Gasser, F. M. Hoffman, Toward a national early warning system for forest disturbances using remotely sensed canopy phenology, Photogrammetric Engineering & Remote Sensing 75(10) (2009) 1150-1156.
3. J. Hartigan, Clustering Algorithms, John Wiley & Sons, 1975.
4. F. M. Hoffman, W. W. Hargrove, Multivariate geographic clustering using a Beowulf-style parallel computer, in: H. R. Arabnia (Ed.), Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'99), ISBN 1- 892512-11-4, Vol. III, CSREA Press, 1999, pp. 1292-1298.
5. W.W. Hargrove, F.M. Hoffman, Potential of multivariate quantitative methods for delineation and visualization of ecoregions, Environmental Management 34(5) (2004) s39-s60. (Pubitemid 40762571)
6. A. K. Jain, M. N. Murty, P. J. Flynn, Data clustering: a review, ACM Comput. Surv. 31 (1999) 264-323.
7. D. Judd, P. McKinley, A. Jain, Large-scale parallel data clustering, Pattern Analysis and Machine Intelligence, IEEE Transactions on 20 (8) (1998) 871 -876.
8. V. S. Sunderam, PVM: a framework for parallel distributed computing, Concurrency: Pract. Exper. 2 (1990) 315-339. URL http://portal.acm.org/citation. cfm?id=95324.95327.
9. W. Gropp, E. Lusk, A. Skjellum, Using MPI: Portable Parallel Programming with the Message Passing Interface, The MIT Press, Cambridge, MA, 1996.
10. I. Dhillon, D. Modha, A data-clustering algorithm on distributed memory multiprocessors, in: M. Zaki, C.-T. Ho (Eds.), Large-Scale Parallel Data Mining, Vol. 1759 of Lecture Notes in Computer Science, Springer Berlin/Heidelberg, 2000, pp. 802-802.
11. Y. Li, S. Chung, Parallel bisecting k-means with prediction clustering algorithm, The Journal of Supercomputing 39 (2007) 19-37, 10.1007/s11227-006- 0002-7. (Pubitemid 44915077)
12. S. J. Phillips, Reducing the computation time of isodata and k-means unsupervised classification algorithms, in: Geoscience and Remote Sensing Symposium, 2002 (IGARSS'02), Vol. 3, 2002, pp. 1627-1629.
13. S. J. Phillips, Acceleration of k-means and related clustering algorithms, in: D. M. Mount, C. Stein (Eds.), ALENEX'02: Revised Papers from the 4th International Workshop on Algorithm Engineering and Experiments, Springer-Verlag, London, UK, 2002, pp. 166-177.
14. F. M. Hoffman, R. T. Mills, J. Kumar, S. S. Vulli, W. W. Hargrove, Geospatiotemporal data mining in an early warning system for forest threats in the United States, in: Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2010), Honolulu, Hawaii (Invited), 2010.