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Geo-spatial Information Science Volume 19, 2016 - Issue 2: Big Data in Geo-spatial Information Science
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Articles

Data field for mining big data

Shuliang WangState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China;School of Software, Beijing Institute of Technology, Beijing, ChinaCorrespondence[email protected]
,
Ying LiState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China
&
Dakui WangInternational School of Software, Wuhan University, Wuhan, China
Pages 106-118 | Received 17 Feb 2016, Accepted 25 Mar 2016, Published online: 27 Jun 2016

References

  • Ben-Bassat, M. 1982. “Pattern Recognition and Reduction of Dimensionality.” In Handbook of Statistics, edited by P.R. Krishnaiah, 773–791. Oxford: Elsevier.10.1016/S0169-7161(82)02038-0
  • Blum, A. L., and P. Langley. 1997. “Selection of Relevant Features and Examples in Machine Learning.” Artificial Intelligence 97 (1-2): 245–271.10.1016/S0004-3702(97)00063-5
  • Caruana, R., and D. Freitag. 1994. “Greedy Attribute Selection.” In Proceedings of the 11th International Conference on Machine Learining, 28–36. New Brunswick, NJ: Morgan Kaufmann.
  • Dash, M., K. Choi, P. Scheuermannn, and H. Liu. 2002. “Feature Selection for Clustering-a Filter Solution.” In Proceedings of the 2002 IEEE International Conference on Data Mining, 115–122. Japan: Maebashi City.
  • Duong, T. and M. L. Hazelton. 2003. “Plug-in Bandwidth Matrices for Bivariate Kernel Density Estimation.” Journal of Nonparametric Statistics 15 (1): 17−30.
  • Fang, M., S. L. Wang, and H. Jin. 2010. “Spatial Neighborhood Clustering Based on Data Field.” In Advanced Data Mining and Applications, 262–269. Berlin Heidelberg: Springer.10.1007/978-3-642-17316-5
  • Faraway, J. J., and M. Jhun. 1990. “Bootstrap Choice of Bandwidth for Density Estimation.” Journal of the American Statistical Association 85 (412): 1119–1122.10.1080/01621459.1990.10474983
  • Guyon, I., and A. Elisseeff. 2003. “An Introduction to Variable and Feature Selection.” The Journal of Machine Learning Research 3: 1157–1182.
  • Hall, M. A. 2000. “Correlation-Based Feature Selection for Discrete and Numeric Class Machine Learning.” In Proceedings of 7th Intentional Conference on Machine Learning, 359−366. San Diego, CA, December 11−13, 2008.
  • Hall, P., and J. S. Marron. 1987. “Estimation of Integrated Squared Density Derivatives.” Statistics & Probability Letters 6 (2): 109–115.
  • Jones, M. C., J. S. Marron, and S. J. Sheather. 1996. “A Brief Survey of Bandwidth Selection for Density Estimation.” Journal of the American Statistical Association 91 (433): 401–407.10.1080/01621459.1996.10476701
  • Kohavi, R., and G. H. John. 1997. “Wrappers for Feature Subset Selection.” Artificial Intelligence 97 (1-2): 273–324.10.1016/S0004-3702(97)00043-X
  • Li, D. R., S. L. Wang, and D. Y. Li. 2006. Spatial Data Mining Theories and Applications. Beijing: Science Press. (in Chinese).
  • Liu, H., and H. Motoda. 1998. Feature Selection for Knowledge Discovery and Data Mining. Boston, MA: Kluwer Academic.10.1007/978-1-4615-5689-3
  • McKinsey Global Institute. 2011. “Big Data: The Next Frontier for Innovation, Competition, and Productivity.” (Technical Report) May 2011, edited by J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. H. Byers. http://www.mckinsey.com/business-functions/business-technology/our-insights/big-data-the-next-frontier-for-innovation.
  • Mitra, P., C. A. Murthy, and S. K. Pal. 2002. “Unsupervised Feature Selection Using Feature Similarity.” IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (3): 301–312.10.1109/34.990133
  • Parsons, L., E. Haque, and H. Liu. 2004. “Subspace Clustering for High Dimensional Data.” ACM SIGKDD Explorations Newsletter 6 (1): 90–105.10.1145/1007730
  • Pyle, D. 1999. Data Preparation for Data Mining. Burlington, MA: Morgan Kaufmann.
  • Roberts, G. O. 1996. “Markov Chain Concepts Related to Sampling Algorithms.” In Markov Chain Monte Carlo in Practice, edited by W. R. Gilks, S. Richardson, and D. J. E. Spiegelhalter, 45–57. London: Chapman and Hall.
  • Ruppert, D., S. J. Sheather, and M. P. Wand. 1995. “An Effective Bandwidth Selector for Local Least Squares Regression.” Journal of the American Statistical Association 90 (432): 1257–1270.10.1080/01621459.1995.10476630
  • Sheather, S. J., and M. C. Jones. 1991. “A Reliable Data-Based Bandwidth Selection Method for Kernel Density Estimation.” Journal of the Royal Statistical Society. Series B (Methodological) 53: 683–690.
  • Silverman, B. W. 1986. Density Estimation for Statistics and Data Analysis. London: CRC Press.10.1007/978-1-4899-3324-9
  • Terrell, G. R. 1990. “The Maximal Smoothing Principle in Density Estimation.” Journal of the American Statistical Association 85: 470–477.10.1080/01621459.1990.10476223
  • Wang, S. L., W. Y. Gan, D. Y. Li, and D. R. Li. 2011. “Data Field for Hierarchical Clustering.” International Journal of Data Warehousing and Mining 7 (4): 43–63.10.4018/IJDWM
  • Yu, L. and H. Liu. 2003. “Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution.” In Proceedings of the Twentieth International Conference on Machine Leaning (ICML-03), 856−863. Washington, DC, August 21−24.

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