Spatial Association Rule Mining Algorithms And Applications

Spatial data mining extracts latent forecasting information and finds the most worthwhile knowledge to guide Scientific Decision Making from spatial database, which is a hot topic of research and application for people. At present, in spatial association rules mining, the mining method based on spatial transaction is a comprehensive applied technology by people, but the technology of forming frequent itemsets and pruning is one of difficult problems when these algorithms are applied to huge spatial data mining.As fast development of digitization electric power system, it is a disquisitive emphasis that spatial data mining is used in electric power system. In Grid Visual Manage System, the efficiency of grid analysis is mainly affected by the number of node and line searched by topology analysis. Because of shortcomings of presented mining algorithms, which inefficiently improve the speed of topology analysis, we need research some efficient algorithms of spatial association rules mining, which are used in Grid Visual Manage System to improve efficiency of grid analysis.In this paper, aiming to the shortage of spatial association transverse mining, namely, in presented these spatial transverse mining algorithms, although these algorithms improve the technology of forming candidate frequent itemsets and pruning, they inefficiently extract monolayer transverse spatial association rules that contain more the number of spatial object. Firstly, an algorithm of spatial transaction mining based on alternate search (ASTMAS) is proposed, which is suitable for mining these association among these different spatial objects from the same spatial pattern. In huge spatial data mining, the algorithm extracts monolayer transverse spatial association rules that contain any the number of spatial objects, through changing the traditional way of forming frequent itemsets and search strategy of presented binary mining algorithms. The algorithm uses two ways of number ascending and descending to double generate candidate frequent itemsets, in order to extract spatial association rules by alternate search. And this algorithm uses number character to reduce the number of scaned spatial transactions when computing support. Simulation experiments indicate that its efficiency is more efficient than presented algorithms. The algorithm is used in Grid Visual Manage System to improve execution efficiency of scope analysis of power supply, via deleting irrelevant devices with power supply and reducing the number of node or line searched by topology analysis, and System Performance Evaluation embodies practicability of algorithm.And then, aiming to these presented mining algorithms based on spatial transaction inefficiently extract multilayer transverse spatial association rules, an algorithm of multilayer spatial transaction mining based on digital ascending (AMSTMDA) is proposed, which is suitable for mining these association among these different spatial objects from these different spatial pattern. In huge spatial data mining, the algorithm extracts multilayer transverse spatial association rules through improving the technology of forming frequent itemsets and saving spatial data. The algorithm uses binary number to express spatial topology association to improve the way of data saving, and uses number ascending to generate candidate frequent itemsets, in order to extract spatial topology association. Simulation experiments indicate that its efficiency is fast and efficient. The algorithm is used in Grid Visual Manage System to improve execution efficiency of analysis of Optimization Power Failure Scheme, via deleting irrelevant devices with power failure and reducing the number of node or line searched by topology analysis, and System Performance Evaluation embodies practicability of algorithm.