Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation. This tutorial will introduce spatial data mining in the following categories: location prediction, spatial outlier detection, and co-location mining.
A General Introduction to Spatial Data Mining
Slides: What′s Special About Spatial Data Mining?(PS,PDF) 数据挖掘研究院
Slides: Spatial Data Mining: Accomplishments and Research Needs, GIScience 2004 Keynote Speech(PS,PDF)
Reference:
[SDM03] Shashi Shekhar, Pusheng Zhang, Yan Huang, and Ranga Raju Vatsavai, "Trends in Spatial Data Mining", as a book chapter to appear in "Data Mining: Next Generation Challenges and Future Directions", Hillol Kargupta and Anupam Joshi(editors), AAAI/MIT Press, 2003 (PS, PDF) 数据挖掘研究院
Location Prediction
Slides: Spatial Autoregressive Models. (PS,PDF) 数据挖掘研究院
Slides: Scalable Parallel Formulations of Spatial Auto-Regression (SAR) Models for Mining Regular Grid Geospatial Data (PPT).
Example of Scalable Parallel Formulations of Spatial Auto-Regression (SAR) Models: 数据挖掘实验室
- Readme
- Codes:
- Matlab Programs & Data-sets (on IBM SP):
For small data set (n=16): (ReadMe, Browse programs & Data-sets, Download all in a zipped file)
For large data set (n=2500): (Readme, Browse programs & Data-sets, Download all in a zipped file) 数据挖掘研究院
- Fortran Programs and Data-sets for 2500 case (on IBM SP):(Readme, Browse programs and code, Download all in a zipped file)
Reference:
[AHP03] Baris Kazar, Shashi Shekhar, and David J. Lilja, "Parallel Formulation of Spatial Auto-Regression", Army High-Performance Computing Research Center (AHPCRC) Technical Report no. 2003-125, August 2003 (PS, PDF)
[IEE02] S. Shekhar, P. Schrater, R. Vatsavai, W. Wu, and S. Chawla, Spatial Contextual Classification and Prediction Models for Mining Geospatial Data , IEEE Transactions on Multimedia (special issue on Multimedia Dataabses) , 2002. (PS, PDF)
Spatial Outlier Detection
Slides: A Unified Approach to Detecting Spatial Outliers (PS, PDF)
Example of Spatial Outlier Detection using Matlab:
- Readme
- Matlab Scripts: (Browse files, Download all in a zipped file)
Reference:
[GEI03] Shashi Shekhar, Chang-Tien Lu, Pusheng Zhang, "A Unified Approach to Detecting Spatial Outliers" volume 7, issue 2, GeoInformatica, Kluwer Academic Publishers, 2003. (PS, PDF)
Co-location Mining
Slides: Mining Co-location Patterns (PS, PDF) 数据挖掘研究院
Example of Mining Co-location Patterns using Matlab:
- Readme
- Matlab Scripts: (Browse files, Download all in a zipped file)
Reference: 数据挖掘实验室
[TKD03] Yan Huang, Shashi Shekhar, and Hui Xiong, Discovering Co-location Patterns from Spatial Datasets: A General Approach, Submitted to IEEE TKDE(under second round review), (PS, PDF)
More publications are available at the Publication Cabinet of Spatial Databases Group 数据挖掘研究院
