Gaussian processes for active data mining of spatial aggregates

Proceedings of the 2005 SIAM International Conference on Data Mining, SDM 2005

Volumn , Issue , 2005, Pages 427-438

  Ramakrishnan, Naren ^a   Bailey Kellogg, Chris ^b   Tadepalli, Satish ^a   Pandey, Varun N ^a  

^a State University   (United States)

^b Dartmouth Department of Computer Science_Dartmouth College   (United States)

Author keywords

Active mining; Gaussian processes; Sparse data; Spatial aggregation; Spatial mining

Indexed keywords

AGGREGATES; COST ENGINEERING; DATA ACQUISITION; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); GLOBAL OPTIMIZATION; SPACE DIVISION MULTIPLE ACCESS; WIRELESS SENSOR NETWORKS;

ACTIVE MINING; FUNCTION APPROXIMATION; GAUSSIAN PROCESSES; HIGH-LEVEL STRUCTURE; SCIENTIFIC AND ENGINEERING APPLICATIONS; SPARSE DATA; SPATIAL AGGREGATION; TRADITIONAL APPROACHES;

DATA MINING;

EID: 84880091394     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972757.38     Document Type: Conference Paper

References (30)

1. C. Bailey-Kellogg and N. Ramakrishnan. Ambiguity-Directed Sampling for Qualitative Analysis of Sparse Data from Spatially Distributed Physical Systems. In Proc. IJCAI, pages 43-50, 2001.
2. C. Bailey-Kellogg and F. Zhao. Influence-Based Model Decomposition for Reasoning about Spatially Distributed Physical Systems. Artificial Intelligence, Vol. 130(2): pages 125-166, 2001.
3. C. Bailey-Kellogg, F. Zhao, and K. Yip. Spatial Aggregation: Language and Applications. In Proc. AAAI, pages 517-522, 1996.
4. K. Brinker. Incorporating Diversity in Active Learning with Support Vector Machines. In Proceedings of the Twentieth International Conference on Machine Learning (ICML'03), pages 59-66, 2003.
5. D.A. Cohn, Z. Ghahramani, and M.I. Jordan. Active Learning with Statistical Models. Journal of Artificial Intelligence Research, Vol. 4: pages 129-145, 1996.
6. D. Cornford, LT. Nabney, and C.K.I. Williams. Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields. In Proceedings of NIPS, pages 861-867, 1998.
7. N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, 2000.
8. C. Currin, T. Mitchell, M. Morris, and D. Ylvisaker. Bayesian Prediction of Deterministic Functions, with Applications to the Design and Analysis of Computer Experiments. J. Amer. Stat. Assoc, Vol. 86: pages 953-963, 1991.
9. R.G. Easterling. Comment on 'Design and Analysis of Computer Experiments'. Statistical Science, 4(4):425-427, 1989.
10. J. Garcke and M. Griebel. Data Mining with Sparse Grids using Simplicial Basis Functions. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 87-96, 2001.
11. M. Gaviano, D.E. Kvasov, D. Lera, and Y.D. Sergeyev. Algorithm 829: Software for Generation of Classes of Test Functions with Known Local and Global Minima for Global Optimization. ACM Transactions on Mathematical Software, Vol. 29(4): pages 469-480, Dec 2003.
12. X. Huang and F. Zhao. Relation-Based Aggregation: Finding Objects in Large Spatial Datasets. In Proceedings of the 3rd International Symposium on Intelligent Data Analysis, 1999.
13. J.-N. Hwang, J.J. Choi, S. Oh, and R.J. Marks II. Query-based Learning Applied to Partially Trained Multilayer Perceptrons. IEEE Transactions on Neural Networks, Vol. 2(1): pages 131-136, 1991.
14. A.G. Journel and C.J. Huijbregts. Mining Geostatistics. Academic Press, New York, 1992.
15. G. Karypis, E.-H. Han, and V. Kumar. Chameleon: Hierarchical Clustering using Dynamic Modeling. IEEE Computer, Vol. 32(8): pages 68-75, 1999.
16. J. Koehler and A. Owen. Computer Experiments. In S. Ghosh and C. Rao, editors, Handbook of Statistics: Design and Analysis of Experiments, pages 261-308. North Holland, 1996.
17. D.J. MacKay. Information-Based Objective Functions for Active Data Selection. Neural Computation, Vol. 4(4): pages 590-604, 1992.
18. I.T. Nabney. Netlab: Algorithms for Pattern Recognition. Springer-Verlag, 2002.
19. R.M. Neal. Monte Carlo Implementations of Gaussian Process Models for Bayesian Regression and Classification. Technical Report 9702, Department of Statistics, University of Toronto, Jan 1997.
20. R.T. Ng and J. Han. CLARANS: A Method for Clustering Objects for Spatial Data Mining. IEEE Transactions on Knowledge and Data Engineering, Vol. 14(5): pages 1003-1016, 2002.
21. I. Ordonez and F. Zhao. STA: Spatio-Temporal Aggregation with Applications to Analysis of Diffusion-Reaction Phenomena. In Proc. AAAI, pages 517-523, 2000.
22. N. Ramakrishnan and C. Bailey-Kellogg. Sampling Strategies for Mining in Data-Scarce Domains. IEEE/AIP CiSE, Vol. 4(4): pages 31-43, 2002.
23. N. Ramakrishnan and C. Bailey-Kellogg. Gaussian Process Models of Spatial Aggregation Algorithms. In Proc. IJCAI, pages 1045-1051, 2003.
24. J. Sacks, W.J. Welch, T.J. Mitchell, and H.P Wynn. Design and Analysis of Computer Experiments. Statistical Science, Vol. 4(4): pages 409-435, 1989.
25. S. Tong and D. Koller. Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research, Vol. 2: pages 45-66, 2001.
26. A. Verstak et al. Using Hierarchical Data Mining to Characterize Performance of Wireless System Configurations. Technical Report cs.CE/0208040, CoRR, Aug 2002.
27. C.K.I. Williams. Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond. In M.I. Jordan, editor, Learning in Graphical Models, pages 599-621. MIT Press, Cambridge, MA, 1998.
28. K.M. Yip and F. Zhao. Spatial Aggregation: Theory and Applications. JAIR, Vol. 5: pages 1-26, 1996.
29. K.M. Yip, F. Zhao, and E. Sacks. Imagistic Reasoning. ACM Computing Surveys, Vol. 27(3): pages 363-365, 1995.
30. F. Zhao, C. Bailey-Kellogg, and M.P.J. Fromherz. Physics-Based Encapsulation in Embedded Software for Distributed Sensing and Control Applications. Proceedings of the IEEE, 91:40-63, 2003.