CSE591 Data Mining

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  • Dunham Pp224-226
  • Pp 234-235


  • 1. 6. Spatial Mining Spatial Data and Structures Images Spatial Mining Algorithms
  • 2. Definitions
    • Spatial data is about instances located in a physical space
    • Spatial data has location or geo-referenced features
    • Some of these features are:
      • Address, latitude/longitude (explicit)
      • Location-based partitions in databases (implicit)
  • 3. Applications and Problems
    • Geographic information systems (GIS) store information related to geographic locations on Earth
      • Weather, community infrastructure needs, disaster management, and hazardous waste
    • Homeland security issues such as prediction of unexpected events and planning of evacuation
    • Remote sensing and image classification
    • Biomedical applications include medical imaging and illness diagnosis
  • 4. Use of Spatial Data
    • Map overlay – merging disparate data
      • Different views of the same area: (Level 1) streets, power lines, phone lines, sewer lines, (Level 2) actual elevations, building locations, and rivers
    • Spatial selection – find all houses near WSU
    • Spatial join – nearest for points, intersection for areas
    • Other basic spatial operations
      • Region/range query for objects intersecting a region
      • Nearest neighbor query for objects closest to a given place
      • Distance scan asking for objects within a certain radius
  • 5. Spatial Data Structures
    • Minimum bounding rectangles (MBR)
    • Different tree structures
      • Quad tree
      • R-Tree
      • kd-Tree
    • Image databases
  • 6. MBR
    • Representing a spatial object by the smallest rectangle [(x1,y1), (x2,y2)] or rectangles
    (x1,y1) (x2,y2)
  • 7. Tree Structures
    • Quad Tree: every four quadrants in one layer forms a parent quadrant in an upper layer
      • An example
  • 8. R-Tree
    • Indexing MBRs in a tree
      • An R-tree of order m has at most m entries in one node
      • An example (order of 3)
    R8 R7 R6 R3 R2 R1 R5 R4 R8 R1 R2 R3 R6 R5 R4 R7
  • 9. kd-Tree
    • Indexing multi-dimensional data, one dimension for a level in a tree
      • An example
  • 10. Common Tasks dealing with Spatial Data
    • Data focusing
      • Spatial queries
      • Identifying interesting parts in spatial data
      • Progress refinement can be applied in a tree structure
    • Feature extraction
      • Extracting important/relevant features for an application
    • Classification or others
      • Using training data to create classifiers
      • Many mining algorithms can be used
        • Classification, clustering, associations
  • 11. Spatial Mining Tasks
    • Spatial classification
    • Spatial clustering
    • Spatial association rules
  • 12. Spatial Classification
    • Use spatial information at different (coarse/fine) levels (different indexing trees) for data focusing
    • Determine relevant spatial or non-spatial features
    • Perform normal supervised learning algorithms
      • e.g., Decision trees,
  • 13. Spatial Clustering
    • Use tree structures to index spatial data
    • DBSCAN: R-tree
    • CLIQUE: Grid or Quad tree
    • Clustering with spatial constraints (obstacles  need to adjust notion of distance)
  • 14. Spatial Association Rules
    • Spatial objects are of major interest, not transactions
    • A  B
      • A, B can be either spatial or non-spatial (3 combinations)
      • What is the fourth combination?
    • Association rules can be found w.r.t. the 3 types
  • 15. Summary
    • Spatial data can contain both spatial and non-spatial features.
    • When spatial information becomes dominant interest, spatial data mining should be applied.
    • Spatial data structures can facilitate spatial mining.
    • Standard data mining algorithms can be modified for spatial data mining, with a substantial part of preprocessing to take into account of spatial information.
  • 16. Bibliography
    • M. H. Dunham. Data Mining – Introductory and Advanced Topics. Prentice Hall. 2003.
    • R.O. Duda, P.E. Hart, D.G. Stork. Pattern Classification, 2 nd edition. Wiley-Interscience.
    • J. Han and M. Kamber. Data Mining – Concepts and Techniques. 2001. Morgan Kaufmann.