Spring 2003Data Mining by H. Liu, ASU1 6. Spatial Mining Spatial Data and Structures Images Spatial Mining Algorithms

Spring 2003Data Mining by H. Liu, ASU2 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)

Spring 2003Data Mining by H. Liu, ASU3 Applications and Problems Geographic information systems (GIS) store information related to geographic locations on Earth –Weather, climate monitoring, 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

Spring 2003Data Mining by H. Liu, ASU4 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 ASU 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

Spring 2003Data Mining by H. Liu, ASU5 Spatial Data Structures Minimum bounding rectangles (MBR) Different tree structures –Quad tree –R-Tree –kd-Tree Image databases

Spring 2003Data Mining by H. Liu, ASU6 MBR Representing a spatial object by the smallest rectangle [(x1,y1), (x2,y2)] or rectangles (x1,y1) (x2,y2)

Spring 2003Data Mining by H. Liu, ASU7 Tree Structures Quad Tree: every four quadrants in one layer forms a parent quadrant in an upper layer –An example

Spring 2003Data Mining by H. Liu, ASU8 R-Tree Indexing MBRs in a tree –An R-tree order of m has at most m entries in one node –An example (order of 3) R8 R1 R2 R3 R6 R5R4 R7 R8 R7R6 R3R2R1R5R4

Spring 2003Data Mining by H. Liu, ASU9 kd-Tree Indexing multi-dimensional data, one dimension for a level in a tree –An example

Spring 2003Data Mining by H. Liu, ASU10 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

Spring 2003Data Mining by H. Liu, ASU11 Spatial Mining Tasks Spatial classification Spatial clustering Spatial association rules

Spring 2003Data Mining by H. Liu, ASU12 Spatial Classification Use spatial information at different (coarse/fine) levels (in different indexing trees) for data focusing Determine relevant spatial or non-spatial features Perform normal supervised learning algorithms –e.g., Decision trees, NBC, etc.

Spring 2003Data Mining by H. Liu, ASU13 Spatial Clustering Use tree structures to index spatial data Cluster locally –DBSCAN: R-tree –CLIQUE: Grid or Quad tree

Spring 2003Data Mining by H. Liu, ASU14 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

Spring 2003Data Mining by H. Liu, ASU15 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.

Spring 2003Data Mining by H. Liu, ASU16 Bibliography M. H. Dunham. Data Mining – Introductory and Advanced Topics. Prentice Hall 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 Morgan Kaufmann.