Study On Techniques Of Spatial Data Mining

Spatial data mining (SDM) 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 universal implicated in spatial data. This thesis studies on the theories, techniques and the applications of SDM. The main content of this thesis include the follows:The definition and characteristics of SDM are set forth, and a structure of spatial data mining system including data source, miner and user interface is put forward. The essential processes of SDM are studied and eight types of rules resulting in mining are discussed. There are several kinds of spatial data mining approaches researched in this thesis.In the thesis we expound the general theories and concepts of the spatial data structure, spatial database and spatial data warehouse, and introduce two typical prototype systems of spatial data database.The theory of Cloud is introduced into SDM domain. The basic notion and character of Cloud, and the extended models are studied. This thesis presents its application in spatial data mining and knowledge discovery, and focuses on the cloud models and their algorithms. A partition algorithm based on the spatial distance of halfcloud and trapezium cloud is given.In this thesis, the definition of spatial association rule is expounded and weresearch on the Apriori algorithm of association rules, probe into several efficient methods for improving the Apriori algorithm. After well discussed the association rules, an improved Apriori algorithm is developed and tested in this thesis.After studying the analysis and comparison of the realization techniques of spatial database system and spatial data mining systems, we propose a development model of spatial data mining system. Using the relative theories above, we design a spatial data mining prototype system based on Mapobjects and Oracle Spatial. We take the land usage of a city as example, and test the system on it.