A Research on Spatial Topological Association Rules Mining

Abstract

Commission II, WG II/3 KEY WORDS:Geography, Research, Data mining, GIS, Algorithms ABSTRACT:Spatial association rules mining is a process of acquiring information and knowledge from large databases. Due to the nature of geographic space and the complexity of spatial objects and relations, the classical association rule mining methods are not suitable for the spatial association rule mining. Classical association rule mining treats all input data as independent, while spatial association rules often show high autocorrelation among nearby objects. The contiguous, adjacent and neighboring relations between spatial objects are important topological relations.In this paper a new approach based on topological predictions to discover spatial association rules is presented. First, we develop a fast method to get the topological relationship of spatial data with its algebraic structure. Then the interested spatial objects are selected. To find the interested spatial objects, topological relations combining with distance were used. In this step, the frequent topological predications are gained. Next, the attribute datasets of the selected interested spatial objects are mined with Apriori algorithm. Last, get the spatial topological association rules. The presented approach has been implemented and tested by the data of GDP per capita, railroads and roads in China in the year of 2005 at county level. The results of the experiments show that the approach is effective and valid. INTRODUCTIONSpatial association rules mining is a technique which mines the association rules in spatial databases by considering spatial properties and predicates [1,2,3] . One important problem is how to get those spatial relations that compose the spatial properties and predicates from the spatial objects, and translate the non-structured spatial relations to structural expression so that they can be mined with the non-spatial data together. There are many researches about spatial association rules mining, Algorithm ARM involving the spatial relations such as direction, distance and topology has been well discussed in some literatures [2,[4][5][6][7] , whereas Fenzhen Su [8] centered on using spatial difference to express how spatial relations affect the interested spatial association rules we can get.As spatial topological relation is one of the most important spatial relations, many literatures about spatial association rules mining fasten on it and presented many ways to get the topological relation, such as RCC (Region Connection Calculus), the classical MBR (minimum bounding rectangle), 9-intersection model. Ickjai Lee etc. Compared different RCC models' efficiency when used to get topological relations of a group of objects. MBR is a fast method to get the rough topological relations, so it is often used with other precise but expensive means. Eliseo Clementini etc.[3] put forward a method mining the spatial objects with uncertainty which uses the objects with a broad boundary to take the uncertainty of spatial information into account. The ...