CSE591 Data Mining

  • 340 views
Uploaded on

 

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
340
On Slideshare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
10
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide
  • Dunham Pp224-226
  • Pp 234-235

Transcript

  • 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.