SPATIAL DATABASES TEXTBOOK
Title : Spatial Databases: A Tour
Authors: Shashi Shekhar and Sanjay Chawla
Publisher: Prentice Hall, 2003 (ISBN 013-017480-7)
Status: Published on June 15th, 2002.For more detail, please click here
RESEARCH PAPERS
Spatial Data Mining
Spatial Databases
Spatio-Temporal Data Mining
Spatio-Temporal Databases
Real Time Search
Learning and Discovery in Databases
Spatial Data Mining
- General Spatial Data Mining
- S. Shekhar, P. Zhang, Y. Huang, R. Vatsavai, Trend in Spatail Data Mining, as a chapter to appear in Data Mining: Next Generation Challenges and Future Directions, H. Kargupta, A. Joshi, K. Sivakumar, and Y. Yesha(eds.), AAAI/MIT Press, 2003, (pdf, PS)
- S. Shekhar, R. Vatsavai, Techniques for Mining Geospatial Databases, as Chapter 22 in Handbook of Data Mining, Nong Ye (Eds.), LEA Publishers, NJ, 2003.
- S. Shekhar, Y. Huang, W. Wu, C.T. Lu, What's Spatial about Spatial Data Mining: Three Case Studies , as Chapter of Book: Data Mining for Scientific and Engineering Applications. V. Kumar, R. Grossman, C. Kamath, R. Namburu (eds.), Kluwer Academic Pub., 2001, ISBN 1-4020-0033-2. (PS, PDF)
- S. Chawla, S. Shekhar, W. Wu, U. Ozesmi, Modeling Spatial Dependencies for Mining Geospatial Data: An Introduction , as Chapter of Geographic Data Mining and Knowledge Discovery. Harvey J. Miller and Jiawei Han (eds.) Taylor and Francis, 2001, ISBN 0-415-23369-0. . (PS, PDF)
- Data Mining on Earth Science Data
- Zhang, P., Steinbach, M., Kumar, V., Shekhar, S., Tan, P., Klooster, S., and Potter, C, Discovery of Patterns of Earth Science Data Using Data Mining, as a chapter to appear in Next Generation of Data Mining Applications, Mehmed M. Kantardzic and Jozef Zurada (editors), IEEE Press, 2004 (pdf )
- Spatial Outlier Detection
- S. Shekhar, C.T. Lu, P. Zhang, A Unified Approach to Spatial Outliers Detection, to appear in Geoinformatica, 7(2), June, 2003. (PS, PDF)
- S. Shekhar, C.T. Lu, P. Zhang Detecting Graph-based Spatial Outliers: Algorithms and Applications, Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, San Francisco, CA, 2001. (PS, PDF). An extended version appeared in International Journal of Intelligent Data Analysis, 6(5), IOS press. . (PS, PDF)
- Spatial Location Prediction
- B. M. Kazar, S. Shekhar, D. J. Lilja, D. Shires, J. Rogers, M. Celik, A Parallel Formulation of the Spatial Auto-Regression Model, to appear in the International Conference on Geographic Information GIS PLANET, May 2005, Lisbon, Portugal.
- S. Shekhar, B. M. Kazar, D. J. Lilja, Scalable Parallel Approximate Formulations of Multi-Dimensional Spatial Auto-Regression Models for Spatial Data Mining, to appear as summary paper in the 24th Army Science Conference, November 2004, Orlando FL, USA. (PDF)
- B. M. Kazar, S. Shekhar, D. J. Lilja, R. R. Vatsavai, R. K. Pace, Comparing Exact and Approximate Spatial Auto-Regression Model Solutions for Spatial Data Analysis, to appear in the Proc. of Third International Conference on Geographic Information Science (GIScience2004), Maryland, USA, October 2004. (PDF)
- B. M. Kazar, S. Shekhar, D. J. Lilja, D. Boley, A Parallel Formulation of the Spatial Auto-Regression Model for Mining Large Geo-Spatial Datasets, to appear in Proc. of 2004 SIAM International Conf. on Data Mining Workshop on High Performance and Distributed Mining (HPDM2004), Florida, USA, April 2004. (PS, PDF)
- S. Shekhar, P. Schrater, R. Vatsavai, W. Wu, and S. Chawla, Spatial Contextual Classification and Prediction Models for Mining Geospatial Data , to appear in IEEE Transactions on Multimedia (special issue on Multimedia Dataabses) , 2002. (PS, PDF)
- S. Chawla, S. Shekhar, W. Wu, U. Ozesmi, Modeling Spatial Dependencies for Mining Geospatial Data , Proc. of the 1st SIAM International Conference on Data Mining Chicago, IL, 2001. (PS, PDF)
- S. Chawla, S. Shekhar, W. Wu and U. Ozesmi, Extending Data Mining for Spatial Applications: A Case Study in Predicting Nest Locations, Proc. Int. Confi. on 2000 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD 2000), Dallas, TX, May 14, 2000.. (PS, PDF)
- S. Chawla, S. Shekhar, W. Wu, Predicting Locations Using Map Similarity (PLUMS): A Framework for Spatial Data Mining , Proc. of the 6th International Conference on Knowledge Discovery and Data Mining, Boston, MA, 2000. (PS, PDF) Talk (Power Point)
- Co-location Mining
- Mete Celik, James M. Kang, Shashi Shekhar, Zonal Co-location Pattern Discovery with Dynamic Parameters, In Proc. of 7th IEEE Int’ l Conf. on Data Mining (ICDM), Omaha, Nebraska, 2007. (PDF)
- Jin Soung Yoo and Shashi Shekhar, A Join-less Approach for Mining Spatial Co-location Patterns, the IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol.18, No.10, 2006. PDF
- Jin Soung Yoo, Shashi Shekhar and Mete Celik, A Join-less Approach for Co-location Pattern Mining: A Summary of Results, in Proc. of the IEEE International Conference on Data Mining(ICDM), Houston, USA, 2005 (Short: PS, PDF), (Full: PS, PDF)
- Jin Soung Yoo and Shashi Shekhar, A Partial Join Approach for Mining Co-location Patterns, in Proc. of the 12th ACM International Symposium on Advances in Geographic Information Systems(ACM-GIS), Washington D.C., USA, 2004 (PS, PDF)
- Hui Xiong, Shashi Shekhar, Yan Huang, Vipin Kumar, Xiaobin Ma, Jin Soung Yoo, A Framework for Discovering Co-location Patterns in Data Sets with Extended Spatial Objects, in Proc. of SIAM International Conf. on Data Mining (SDM), Florida, USA, 2004. (PDF)
- Yan Huang, Shashi Shekhar, and Hui Xiong, Discovering Co-location Patterns from Spatial Datasets: A General Approach, IEEE Transactions on Knowledge and Data Engineering (TKDE), 16(12), pp. 1472-1485, December 2004, (PS, PDF)
- Yan Huang, Hui Xiong, Shashi Shekhar,and Jian Pei, Mining Confident Co-location Rules without A Support Threshold, to appear in Proc. of 18th ACM Symposium on Applied Computing (ACM SAC), Melbourne, FL, March 2003 (PS, PDF)
- S. Shekhar and Y. Huang, Discovering Spatial Co-location Patterns: A Summary of Results , Proc. of 7th International Symposium on Spatial and Temporal Databases(SSTD01), L.A., CA, July 2001. (PS, PDF)
- Hot Spot Discovery - Crime Analysis
REAL TIME SEARCH
LEARNING AND DISCOVERY IN DATABASES