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6. Spatial Mining. Spatial Data and Structures Images Spatial Mining Algorithms. 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)

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6 spatial mining

6. Spatial Mining

Spatial Data and Structures

Images

Spatial Mining Algorithms

Spatial Data Mining

G Dong (WSU) & H. Liu (ASU)


Definitions
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)

Spatial Data Mining

G Dong (WSU) & H. Liu (ASU)


Applications and problems
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

Spatial Data Mining

G Dong (WSU) & H. Liu (ASU)


Use of spatial data
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

Spatial Data Mining

G Dong (WSU) & H. Liu (ASU)


Spatial data structures
Spatial Data Structures

  • Minimum bounding rectangles (MBR)

  • Different tree structures

    • Quad tree

    • R-Tree

    • kd-Tree

  • Image databases

Spatial Data Mining

G Dong (WSU) & H. Liu (ASU)


MBR

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

(x2,y2)

(x1,y1)

Spatial Data Mining

G Dong (WSU) & H. Liu (ASU)


Tree structures
Tree Structures

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

    • An example

Spatial Data Mining

G Dong (WSU) & H. Liu (ASU)


R tree

R6

R8

R1

R7

R2

R3

R4

R5

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

R6

R7

R1

R2

R3

R4

R5

Spatial Data Mining

G Dong (WSU) & H. Liu (ASU)


Kd tree
kd-Tree

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

    • An example

Spatial Data Mining

G Dong (WSU) & H. Liu (ASU)


Common tasks dealing with spatial data
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

Spatial Data Mining

G Dong (WSU) & H. Liu (ASU)


Spatial mining tasks
Spatial Mining Tasks

  • Spatial classification

  • Spatial clustering

  • Spatial association rules

Spatial Data Mining

G Dong (WSU) & H. Liu (ASU)


Spatial classification
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,

Spatial Data Mining

G Dong (WSU) & H. Liu (ASU)


Spatial clustering
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)

Spatial Data Mining

G Dong (WSU) & H. Liu (ASU)


Spatial association rules
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

Spatial Data Mining

G Dong (WSU) & H. Liu (ASU)


Summary
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.

Spatial Data Mining

G Dong (WSU) & H. Liu (ASU)


Bibliography
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.

Spatial Data Mining

G Dong (WSU) & H. Liu (ASU)


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