Study On Spatial Data Mining Techniques Based On Cloud Theory

Spatial data mining refers to picking up interesting rules from spatial database, such as spatial patterns and characteristics, the universal relations of spatial and non-spatial data, and other data characteristics implicated in spatial data. This thesis studies on the theories, techniques and the applications of spatial data mining. The main content of this thesis include the follows:introduced the definition and the characteristics of spatial data mining, studied the uncertainty of spatial data with emphasis uncertainty classification and processing method.introduced cloud theory concept, cloud model and it's expansion, focus on discussion about cloud model expression and production method of spatial concept, attribute spatial soft division and concept promotion of cloud model, the contribution of some fixed quantity value to qualitative concept, as well as spatial databases uncertainty inquiry based on cloud model.introduced the basic concept of spatial association ruler, terminology and the methods commonly used in spatial data mining, prospected the research aspects in the spatial association rules mining algorithm.On the bases of improvement of traditional algorithms, developed the new spatial association rule mining algorithm, produced data mining from hydrological geological data of the Shaanxi province from 1980 to 2000, the knowledge obtained may be useful to planning and protection of hydrological recourse.