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Three General Approaches in SDM
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Classroom Contents
Mining Spatial and Spatio-Temporal Datasets: Challenges and Approaches
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- 1 Intro
- 2 What is Special about Mining Spatial Data?
- 3 Why Data Mining?
- 4 Spatial Data Mining (SDM)
- 5 Hotspots, Spatial Cluster
- 6 Complicated Hotspots
- 7 Spatial Outliers
- 8 Predictive Models
- 9 What's NOT Spatial Data Mining
- 10 Relationships on Data in Spatial Data Mining
- 11 OGC Simple Features
- 12 Research Needs for Data
- 13 Statistics in Spatial Data Mining
- 14 Overview of Statistical Foundation
- 15 Spatial Autocorrelation (SA)
- 16 Spatial Autocorrelation: Distance-based measure
- 17 Illustration of Cross-Correlation
- 18 Spatial Slicing
- 19 Edge Effect
- 20 Research Challenges of Spatial Statistics
- 21 Three General Approaches in SDM
- 22 Overview of Data Mining Output
- 23 Illustrative Application to Location Prediction
- 24 Prediction and Trend
- 25 Research Needs for Spatial Classification Open Problems
- 26 Clustering
- 27 Trends Spatial-Concept & Theory-Aware Patterns
- 28 Association Rules - An Analogy
