Spatial%20Data%20Mining

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6. Spatial Mining

• Spatial Data and Structures
• Images
• Spatial Mining Algorithms

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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)

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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

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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

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Spatial Data Structures

• Minimum bounding rectangles (MBR)
• Different tree structures
• Quad tree
• R-Tree
• kd-Tree
• Image databases

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MBR

• Representing a spatial object by the smallest
  rectangle (x1,y1), (x2,y2) or rectangles

(x2,y2)
(x1,y1)
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Tree Structures

• Quad Tree every four quadrants in one layer
  forms a parent quadrant in an upper layer
• An example

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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
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kd-Tree

• Indexing multi-dimensional data, one dimension
  for a level in a tree
• An example

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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

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Spatial Mining Tasks

• Spatial classification
• Spatial clustering
• Spatial association rules

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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,

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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)

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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

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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.

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Bibliography

• M. H. Dunham. Data Mining Introductory and
  Advanced Topics. Prentice Hall. 2003.
• R.O. Duda, P.E. Hart, D.G. Stork. Pattern
  Classification, 2nd edition. Wiley-Interscience.
• J. Han and M. Kamber. Data Mining Concepts and
  Techniques. 2001. Morgan Kaufmann.