Association rule (AR) mining represents a challenge in the field of data mining. Mining ARs using traditional algorithms generates a large number of candidate rules, and even if we use binding measures such as support, reliability, and lift, there are still several rules to keep, and domain experts are needed to extract the rules of interest from the remaining rules. The focus of this paper is on whether we can directly provide rule rankings and calculate the proportional relationship between the items in the rules. To address these two questions, this paper proposes a modified FP-Growth algorithm called FP-GCID (novel FP-Growth algorithm based on Cluster IDs) to generate ARs; in addition, a new method called Mean-Product of Probabilities (MPP) is proposed to rank rules and compute the proportion of items for one rule. The experiment is divided into three phases: the DBSCAN (Density-Based Scanning Algorithm with Noise) algorithm is used to cluster the geographic interest points and map the obtained clusters into corresponding transaction data; FP-GCID is used to generate ARs, which contain cluster information; and MPP is used to choose the best rule based on the rankings. Finally, a visualization of the rules is used to validate whether the two previously stated requirements were fulfilled.
Spatial relation is one of the most important features of geographic space information. The in-depth research on spatial relation is the base of realizing GIS spatial reasoning. Recently GIS software could only express few spatial relations and is difficult to be understood by computer, and also the researches on spatial relation ignore commonsense spatial cognition of people. The process on spatial relation reasoning causes serious semantic collision. Ontology theory was introduced to research spatial relation, which accorded with the commonsense spatial cognition of people. The paper designed a whole frame of space ontology and geospatial relation ontology model, which realized the expression of that model with TBC software. The paper built geographical ontology instance base based on Region data of Henan province and realized the expression of spatial relation ontology with OWL language. Some application instances were also done to validate the theories and technologies of the paper. The final aim is to promote the share and reuse of spatial relation knowledge.
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