DATA WAREHOUSING AND MINIG LECTURE NOTES-- Spatial Data mining:

Spatial Data mining:

            Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation

            The main difference between data mining in relational DBS and in spatial DBS is that attributes of the neighbors of some object of interest may have an influence on the object and therefore have to be considered as well. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations)  which are used by spatial data mining algorithms.  Therefore, new techniques are required for effective and efficient data mining.

            Our framework for spatial data mining is based on spatial neighborhood relations between objects and on the induced neighborhood graphs and neighborhood paths which can be defined with respect to these neighborhood relations. Thus, we introduce a set of database primitives or basic operations for spatial data mining which are sufficient to express most of the spatial data mining algorithms from the literature. This approach has several advantages. Similar to the relational standard language SQL, the use of standard primitives will speed-up the development of new data mining algorithms and will also make them more portable. Second, we can develop techniques to efficiently support the proposed database primitives (e.g. by specialized index structures) thus speeding-up all data mining algorithms which are based on our database primitives. Moreover, our basic operations for spatial data mining can be integrated into commercial database management systems. This will offer additional benefits for data mining applications such as efficient storage management, prevention of inconsistencies, index structures to support different types of database queries which may be part of the data mining algorithms.

 

Spatial Neighborhood Relations, Spatial Neighborhood Graphs and their Operations

 

            Our database primitives for spatial data mining are based on the concepts of neighborhood graphs and neighborhood paths which in turn are defined with respect to neighborhood relations between objects. There are three basic types of spatial relations: topological, distance and direction relations which may be combined by logical operators to express a more complex neighborhood relation. Spatial objects such as points, lines, polygons or polyhedrons are all represented by a set of points. For example, a polygon can be represented by its edges (vector representation) or by the points contained in its interior, e.g. the pixels of an object in a raster image (raster representation).